{"title":"Detecting dirty data using SQL: Rigorous house insurance case","authors":"James G. Lawson, Daniel A. Street","doi":"10.1016/j.jaccedu.2021.100714","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":35578,"journal":{"name":"Journal of Accounting Education","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jaccedu.2021.100714","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Accounting Education","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0748575121000014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 4
Abstract
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.
期刊介绍：
The Journal of Accounting Education (JAEd) is a refereed journal dedicated to promoting and publishing research on accounting education issues and to improving the quality of accounting education worldwide. The Journal provides a vehicle for making results of empirical studies available to educators and for exchanging ideas, instructional resources, and best practices that help improve accounting education. The Journal includes four sections: a Main Articles Section, a Teaching and Educational Notes Section, an Educational Case Section, and a Best Practices Section. Manuscripts published in the Main Articles Section generally present results of empirical studies, although non-empirical papers (such as policy-related or essay papers) are sometimes published in this section. Papers published in the Teaching and Educational Notes Section include short empirical pieces (e.g., replications) as well as instructional resources that are not properly categorized as cases, which are published in a separate Case Section. Note: as part of the Teaching Note accompany educational cases, authors must include implementation guidance (based on actual case usage) and evidence regarding the efficacy of the case vis-a-vis a listing of educational objectives associated with the case. To meet the efficacy requirement, authors must include direct assessment (e.g grades by case requirement/objective or pre-post tests). Although interesting and encouraged, student perceptions (surveys) are considered indirect assessment and do not meet the efficacy requirement. The case must have been used more than once in a course to avoid potential anomalies and to vet the case before submission. Authors may be asked to collect additional data, depending on course size/circumstances.
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