Warner: I started Gigg back in January of 2013. I was originally set up to help musicians, artists, both big and small, get their brand out to the masses, help them share their music with the world and, predominantly, through the social media channels. Social media is changing the world, I think it's the future of advertising in every sense of the word. So, it really fascinated me and I have always had a tremendous love for the arts or for music. As time went on, we realized that what we were creating for artists to help them build their brand, and monetize, and align with new fans and find their biggest fans, we realized that it worked for everybody. So, the Gigg platform really helps everyone with trying to share their art, I guess you could say, or their passion with the world using different social media strategies as well as helping them understand the data behind it. Information, understanding knowledge around data is crucial to any business really, really building what it is that they're trying to build. You need to know and understand, see that stuff. So, that's what we're really focused on doing, is helping businesses or influencers build their brand and understand it using data. Inside of any business, data is imperative. You shouldn't be making any decisions unless you have data behind it, and of course you're going to have to start from scratch at one point and you're going to have to learn trial and error, but once you start creating data, you have to understand it. Depending on the metrics of your business, once you know what those metrics are, you need to build data sets around it. So, as a business owner, it's imperative that you understand what your objectives are because once you understand those objectives you can start putting data sets together. So for us, a lot of people really struggled knowing when to post on social media during the day. They don't understand what seems to be working or not working, they don't understand click-through rates, they don't understand what seems to catch the eye and so a lot of the data that we focus on building is to understand those important pieces of information. So, I've had a chance to work with tons of companies and they're all interested in data. One of the things that many of them struggle with is they really have not defined what it is that they're trying to accomplish or do. They don't understand their objectives. So a lot of times I'll spend time getting to know what their business is about to then identify what data sets they should be building around or seek to better understand. So, I think companies that don't understand what their objectives are or how they can win, you'll never be able to build great data sets around it. I've always been fascinated by companies that have learned how to utilize data to their advantage. There's a lot of companies out there that really struggle with data, but there are few that have figured out how to capitalize on data and because they understand the data around the objectives that they've kind of put together as a team, the sales go skyrocketing, be more efficient with your employee base. I've seen companies that have really, really dialed in how many employees they need on the floor based on data, based on who's coming into their store and when they're there, when they're not there, because there are tons of companies that pay countless dollars for employees that really are not needed at that hour and so understanding the data behind why you need employees at this time, or not, really can help you save or make a lot of money. Guymon: As Scott Warner mentioned, data can provide a tremendous amount of insight if it's analyzed correctly. It's easy to get caught up in the beauty of data visualizations and the magic of forecasts. However, if the insights don't somehow lead to action, then the impact will be short-lived. In the FACT Framework, the C represents the importance of making calculations with the data. Part of making the calculations is knowing what calculations are possible or what algorithms are available. Even if you're not the one who will be making the calculations, it will be helpful for you to know how the algorithms can be applied. In this module, you'll learn how the regression algorithm can be applied to fit a wide variety of relationships among data. Specifically, you'll learn how to set up the data and run a regression to estimate the parameters of non-linear relationships and categorical independent variables. You'll also investigate if the effect of an independent variable depends on the level of another independent variable by including interaction terms in the multiple regression model. Another aspect of this module is learning how to evaluate models, regression or otherwise, to find the most favorable levels of the independent variables. For models that explain revenue, the most favorable levels of the independent variables will maximize revenue. In contrast, if you have a model that describes costs, like a budget, then the most favorable levels of the independent variables will minimize costs. Optimizing models can be difficult because there are so many inputs and constraints that need to be managed. In this module, you'll learn how to use the Solver Add-In in Excel to find the optimal level of inputs. I love the Solver and I think it's an underutilized tool. For some models, the dependent variable is a binary variable that has only two values such as true, false; win, lose; or invest, not invest. In these situations, a special type of regression called logistic regression is used to predict how each observation should be classified. You'll learn about the logic transformation that's used to convert a binary outcome to a linear relationship with the independent variables. Excel doesn't have a built-in logistic regression tool, so you'll learn how to manually design a logistic regression model and then optimize the parameters using the Solver Add-in tool. As you learn about these concepts, you'll also learn about some very useful Excel functions. I hope that by the end of this module you'll have a much better understanding of the tools that assist in making calculations with the data so that you'll know how to create not just insight, but actionable insight.