Tableau and Power BI Project: Data Visualization to Aid Decision$-$Making Adapted from Hoelscher and Mortimer $(2018)$ Introduction to data analytics In this case study, you will gain an understanding of company data and be asked to transform data into useful information to aid in the decision$-$making process. Big data and data analytic topics continue to make headlines in a myriad of media outlets. Big data refers not only to the quantity of data now available $($volume$),$ but also the variety $($form of data$),$ velocity $($analysis of streaming data$),$ and veracity $($uncertainty or quality$)$ of data $($IBM Big Data & Analytics Hub $2017).$ With roughly $2\mathrm{.}5$ quintillion bytes of data being created each day, and over $90\%$ of all data in existence created within the last two years, there is now more data than ever for companies to capture, secure$,$ analyze, and report. This digitization and automation across all facets of organizational processes has become known as Industry $4\mathrm{.}0,$ or the fourth Industrial Revolution. With more companies capturing more data, the market demand for data analysis continues to grow. Accounting firm PwC$,$ recently conducted a survey of roughly $2000$ industrial companies across $26$ countries and found $33\%$ of companies have achieved advanced digitization, but $72\%$ expect to do so by $2020($PwC$,2017).$ Many companies struggle to process, analyze, and utilize the copious amounts of data they are now capturing. PwC expects companies to spend $5\%$ of annual revenues on Industry $4\mathrm{.}0$ capabilities, including robust data analytic technologies, processes and experienced personnel $($PwC$,2017).$ Recent literature suggests that the first obstacle is turning raw data from different sources into something that can be used to make decisions $($Sivarajah$,$ Kamal, Irani, & Weerakkody, $2017).$ The amount of data can be overwhelming to organize and interpret, and managers can have a hard time sorting through the data to determine what is important. Without the ability to interpret and derive meaning from the inux of data, it is of no use. Data analysis requires a different skill set from traditional financial accounting. It requires individuals to go beyond line items in the financial statements and, instead, examine large data sets to identify trends, make predictions regarding future performance, and acquire a better understanding of company data to drive key business decisions based on empirical evidence. Accountants are increasingly being asked to go beyond their traditional roles and analyze large data sets in order to provide support to management $($Beaman & Richardson, $2007$; Romney & Steinbart, $2015).$ It is clear that many companies are still trying to figure out how to use such large amounts of data to their advantage. Extant literature describes the importance of education on data analytics by saying the human element is one of the biggest challenges to the growth of analytics. In addition, in order to gain the biggest advantage from analytics, all workers $-$ not just a few analytics experts $-$ should