[MUSIC] Hello, everyone, healthcare analytics and informatics are exploding fields. People from various organizations and sectors of healthcare are very excited to create new medical technologies, medicines, and treatments. Other groups are motivated to improve the delivery and quality of healthcare and, of course, reduce costs. Finally, groups in public health want to prevent diseases and other social problems from taking hold in our communities. And within this excitement and optimism, many people are quick to say, let's start working on predictive analytics. Let's hire smart data scientists and stratify our populations by risk. Let's display all this dynamic information on web-based dashboards that have real-time data. I've been part of all these types of projects, and I'm also excited to get going with these steps. However, I have learned over and over that sometimes people focus on the final outcomes, or the models and the visualizations, and forget about all the hard work that has to occur to get the data ready for analysis. And by now, you have seen many of these steps in previous course modules. The purpose of this module is to review the importance of creating an analytical plan that clearly specifies the necessary steps to achieve the desired goals. For example, a hospital might be very excited to drive quality improvement with new performance metrics. Yet, they may get too excited to create their own metrics without considering which ones might have already been developed using national standards. Next, once you have clear objectives, it is necessary to have a specific plan about how the data will be extracted and transformed so that the desired information can effectively be obtained. Finally, data scientists, statisticians and visualization experts will have specific constraints and preferences about how the data should be structured to be effective inputs for their algorithms. After this module, you will be able to describe logical processes used by database and statistical programmers to extract, transform, and load or ETL data into structures required for solving medical problems. You will also be able to harmonize data from multiple sources and prepare integrated data files for analysis. This is where the fun is, so let's get started.