Speaker 1: We live in a world that runs on data. It's how Amazon and Netflix know which movies and products to recommend, how Starbucks manages a global supply chain, and how Uber connects drivers with passengers in real time. But the thing is, data skills aren't just for tech companies or professional analysts anymore. Everyone works with data to some degree, and everyone can benefit from data literacy skills. In this video, we're covering an important topic that will help you take your data literacy to the next level. Let's take a few minutes to unpack this concept of data-driven decision making. So remember from our last lesson, we talked about the fact that data is one of the most important and valuable assets that a business can own, but that analytical thinking and data literacy skills are what brings it to life and gives it meaning. Now when I think about what data literacy means in practice, again, it's really all about the process of translating raw data into meaningful information, and then using that information to derive insights that can help inform or influence decisions. And here's a callout that might sound obvious at first, but is something that a very large percentage of data professionals lose sight of, which is that the goal of any analysis is to inspire action. We don't earn our pay by analyzing data or building beautiful charts and dashboards. That's what an analyst does, but it's the why that really matters, which is to inform decisions that lead to real-world outcomes. So how exactly do you go from data to decisions? Here's our framework for data-driven decision making, which is loosely based on the popular DIKW pyramid, which stands for Data, Information, Knowledge, and Wisdom. The idea here is that the further you move down this path, the more value you deliver. So at the top, we have data in its most raw, unprocessed state, and in this format, it has very little meaning or value. Just like oil, data needs to be mined, refined, and repackaged before it becomes useful for analysis. Now once that data is processed, organized, and stored, it can be analyzed to capture useful information, which adds context and clarity for end-users. The third stage is transforming that information into data-driven insights, which are meaningful findings that can help inform recommendations and influence decisions. And last but certainly not least, the final and arguably most important step is to use that insight to inspire stakeholders to take action. Because again, no matter how insightful the analysis, it only adds value when it impacts real decisions. So let's run through an example to see this process in action. An example of the data stage would be saying something like, there were 173 transactions in January. Okay, that's a start, but we don't have any context to make that number meaningful. Is 173 transactions good? Is it bad? Is it better or worse than last month or the same time last year? How does it compare against our benchmarks or forecasts? Turning that data into information might look like this. There were 173 transactions in January, up 75% over December. Fitness equipment and athletic apparel saw the largest month-over-month gains. So with context, we now have some useful and meaningful information to work with. But this still doesn't quite qualify as an insight. It tells us what's happening, but doesn't address why. An insight might sound something like this. Every January, we see an uptick in sales and revenue, driven primarily by new customers looking to prioritize health and fitness in the new year. Now we're getting at some information that we can use to make smart, data-driven recommendations for the business. And that takes us to our final step in inspiring action. Based on this data, we recommend increasing advertising budgets and testing campaigns to promote top-selling health and fitness products in January. This is a clear, data-driven recommendation that ideally will drive a decision and actions that deliver a real impact for the business. Now if you look closely enough, you'll see examples of data-driven decision making everywhere you look. Virtually all of the world's top companies leverage data for everything from product design to pricing strategy, risk mitigation, financial modeling, process optimization, and much, much more. Data is how companies like Netflix and Spotify decide what movies and songs to recommend to you, how Amazon seems to magically know what product you're likely to buy next, how sports teams evaluate talent, how banks automatically detect fraud, how retailers like Starbucks manage complex global supply chains, and how Uber connects drivers with passengers in real time. None of this would be possible without data. If you enjoyed this content and want to see more, we've got a brand new Data Literacy Foundations course, and it's entirely free. You can check it out at mavenanalytics.io. So whether you're an individual looking to build confidence, a leader seeking to empower and upskill your team, or a data professional just trying to stay ahead of the curve, this is the course for you. We've got a lot to cover, so let's dive in.