Proficiency with data analytics is an increasingly important skill within in the accounting profession. However, successful data analysis requires clean source data (i.e., source data without errors) in order to draw reliable conclusions. Although users often assume clean source data, this assumption is frequently incorrect. Therefore, identifying and remediating “dirty data” is a prerequisite to effective data analysis. You, an accountant working at a firm that specializes in data analytics, have been hired by Rigorous House Insurance to analyze the company’s claim insurance data. In addition to investigating specific issues mentioned by the company’s controller, you are tasked with identifying any other data integrity issues that you encounter and providing preventative information system internal control suggestions to the client to mitigate these issues in the future.

精通数据分析是会计行业中越来越重要的技能。但是，成功的数据分析需要干净的源数据（即没有错误的源数据），以便得出可靠的结论。尽管用户经常假设原始数据，但这种假设通常是不正确的。因此，识别和补救“脏数据”是有效数据分析的前提。您是在一家专门从事数据分析的公司工作的会计师，已被Rigorous House Insurance雇用来分析公司的理赔保险数据。除了调查公司控制者提到的特定问题外，