[MUSIC] In this video, we will cover three objectives. The first is to identify different tools that are available to analyze data. Second, we will describe different approaches that are used in analyzing and visualizing data. And lastly, we will explain real-life scenarios based on case studies. Let's talk about some tools that we use with data. There are two broad concepts when leveraging the data assets of an organization. The first is analyzing the data, and the second is visualizing. There are some common tools that we use in data analysis, which are SAS, SPSS, STATA, Minitab, R, Python, and Matlab. You might be familiar with these tools, especially in the academic world because these tools are used to publish papers, conduct studies, and model some physiological or electrical models. For example, in academia where we don't know what's going to happen, but we can use the probabilities of something happening to create a potential outcome or create simulation models. They are designed to run statistical analyses for significance testing, and predictive modeling, and much more. Next we have the visualization tools, which are not as rigorous in terms of statistical analysis, but they are capable of extracting insight from a large amount of data within seconds. Some of these tools are SAP BusinessObjects, Tableau, Qlikview, Microsoft Power BI, IBM Cognos. And then Oracle, Spotfire, or some other vendors or companies that create these, what we call, business intelligence solutions that really create visualization products for consumption by the users. They are really designed to rapidly and broadly extract insight from the data. There is some overlap between the two, but they require different expertise. And we will cover some of those in the following slides. Before we get to the examples of data analysis and visualization, which are quite important, there is a foundational work that needs to be done before we do either of those two. And we call that data management. Data management is a critical skill to succeed in both of data analysis and visualization. It's the ability to source the data, and structure the data, as well as model the data so that it can be consumed, whether you're doing the analysis or visualization in the most effective and efficient way. This requires some knowledge of database management programs such as Microsoft SQL, the Oracle, or SAP databases. And there are some other popular databases like Hadoop, which really is good with a large amount of data and real-time processing. And it's important to know these, although data management is usually given to the traditional information technology staff. But it is important for a person doing the data analysis or the visualization to understand the concepts behind the data management. Before we get to the specific examples and best practices, the applicability of this video is really broad. The tools and methods discussed in this section are related to healthcare quality improvement. However, they can be easily applied in other areas of healthcare, as well as in other industries.
