Required information [The foii'owing information appiies to the questions dispi'ayed beiowj Lab Insight: Auditors and data analysts use data reduction techniques to narrow down transactions from a big dataset to a smaller dataset of transactions that might contain fraud or errors. This is a way for analysts to effectively decrease their effort and increase their work efficiency by examining those transactions that require more attention. A widely used approach in data reduction is called \"fuzzy matching," which serves as a means of merging two datasets using the similarity of a common variable. Users can set a specific threshold for the similarity ofthe common variable of each dataset to merge the datasets based on how similar the common variables are. In addition, fuzzy matching could be applied to combine datasets generated by different accounting information systems. For example. a name in one dataset might be listed as Vern Richardson and Vernon Jay Richardson in another dataset. The use ofa fuzzy match would allow the name to be the common variable. especially ifinformation from each dataset merged into one dataset. Fuzzy matching can also be used to find strings that match approximately. In this lab. we take the perspective of an auditorto reduce data by 1. highlighting suspicious transactions made by the employees and 2. merging datasets from different accounting information systems. (Note: The fuzzy matching addin is not available on Excel for Mac.) Required: Use fuzzy matching to merge data from two different accounting systems. Ask the Question: How can fuzzy matching be used to merge data from two different accounting systems? Software needed - Excel - Screen capture tool (Windows: Snipping Tool: Mac: Cmd+Shift+4} Data: Ait Lab 7-5 Dataxlsx Perform the Analysis: Refer to Lab 75 Alternate in the text for instructions and Lab 75 steps for each the of lab parts. Note: Submit your answers for the foilowing Analysis Questions in the text entry fieid below. 1. Forthis lab. what does a low similarity score mean? 2. For this lab, which would you investigate? Those with high similarity scores. or those with low similarity scores