Speaker 1: In this video, I'm going to show you how to turn data into insights. This is really sort of the key differentiator or distinction that we're trying to hit here. Data on its own, it's just numbers. What we're trying to get, what we're hoping to learn is insights. Something that tells us about our customers or our products or services. So let's talk about that in today's video. Let's jump in. Hi there. My name is Ruben and I'm a data strategist or a professional treasure hunter. And I help companies find those hidden insights or treasures within their data. Not just the actual numbers, but people, decisions, and just how the company works in general. In a few minutes, I'm going to give you a breakdown, how I think about data and how to find those interesting insights. Let's start with COVID-19. So I'm filming this and still in the middle of COVID-19. This is January, 2021. What was fascinating about COVID-19, among many things, was they brought back an idea from the 1930s, speakeasies. You know, back then alcohol was banned, all this illegal bars popped up and you had to kind of know where they are, secret codes, all that kind of stuff. Now COVID-19 had a similar thing, but it wasn't alcohol. People were buying alcohol all over the place. And it was, I think, deemed essential in some countries or provinces. But we saw that with fitness. Some restrictions completely shut down gyms and people were going to all this illegal classes. You know, I say illegal in quotes because, you know, it was against restrictions, but they still wanted to work out. And I can just imagine people walking around with their yoga mats, trying to find their, you know, their yoga class, which is tempted and so on. So I thought that was hilarious. The point here is that you had to know which studio was doing this, if it wasn't publicly advertised, just like the speakeasies. And it becomes a hidden door. And I think that's what insights are within your data. They are hidden doors. We need to know where to look so we can find them. And to do that, we have one simple assessment that you can start using today. I spoke about this before in previous videos. This is the past, present, future assessment. You try to determine how well you can understand the past, what happened, the present, what is going on, and the future, what will happen. And depending on where you rank, this is where you start to figure out where you can start looking for insights. In the past, you probably want something like a data analyst to dig through the data and try to find patterns or distinctions. In the present, you're looking at something like business intelligence. You want to surface data as it comes, as the performance of a campaign is running, a product launch, whatever it may be. In the future, we're trying to figure out what will happen. So we need a data scientist. So based on those three stages, then we can determine where to spend resources and what data we need. Under each of these areas, I want you to think about three questions or three ways to rank yourself. First is insights. How many insights are you actually extracting from each of these stages? You know, when you look at the past data, are you getting insights from it, the present data, future data? Second is decisions. Is this actually helping you make decisions? Is it helping you change your products or how you approach your customers or how you build your teams or how you even do strategy at a company-wide level? And third is clarity. Do you actually feel like you understand what's going on? You know, at the end of the day, we're simply trying to figure out what's happening and what we should do about it. That's really it. This is the process where we need to dig into the data, find those insights, find those patterns, and try to figure out what is going on. You know, I work with a governmental client here in British Columbia, and we took their 25, 30 different disparate data sources, and we brought them together in a dashboard. And the whole point of it was to have a central place where people could look at what was going on, what were some of the high-level metrics, and do it in a quick fashion. So the dashboard has summaries, small widgets, and it had longer things that you could dive into. So if you only had a few minutes, there was a section just for you. And it will tell you high-level, this is what's going on, this is what you should know. You know, this is sort of like the briefing that, you know, presidents get in the morning. Very short, to the point, this is what's going on in the world. But if you had more time, there were widgets that you could dive into, filter, play around with. And that's really what you also want to do with your data, how you interact with it, how you work with it. Now, I don't think it's really about breaking apart the data. This process might be done by a data analyst. It might not be done by yourself. The overall strategy in which you need to take to understand your data and then turn that into insights. And from there, decisions. And that's all we have for today. If you enjoyed this video, make sure to like, subscribe, look for those buttons all over this video. I post videos on a weekly basis. So there's going to be breakdowns, technology, insights, case studies, a bunch of stuff. So make sure to subscribe and you'll be notified when those hit your YouTube inbox, whatever you call it. In the description, you're also going to see two links. One is to my weekly newsletter. I call it The Growth Needle. So I cover similar ideas in a text format. And some things only go out on the newsletter. So that's free. You should check that out. And there's also going to be a link for Twitter. I just started using Twitter, sharing, you know, small ideas through Twitter. So another way to follow me. Find a question for you in the comments below. Just let me know what's one hidden door that you found recently in your data or that you're hoping to find. So let me know. I'd love to hear from you.