Data analytics can be defined as "the process of gathering and analyzing data and then using the results to make better decisions" (Stippich and Preber, Data Analytics: Elevating Internal Audit's Value (Institute of Internal Auditors Research Foundation 2016)). Under this definition, data analytics is clearly a process that organizations have always attempted to optimize. Yet today, with so much electronic data available from various sources, the art of data analytics is more sophisticated than ever, leaving organizations at the edge of a new frontier of analysis.
So, how well-positioned are accountants to face this new frontier? To get a preliminary idea, consider the discussions and related exhibits on data analytics produced by the Institute of Internal Auditors Research Foundation (IIARF) and Grant Thornton in the book Data Analytics, cited above; while written from the perspective of an organization's internal audit function, this book applies equally well to public accounting firms and related institutions. From its analyses, two takeaways emerge: (1) At a high level, accountants understand how planning a strong data analytics function can provide value to an organization; but, (2) at a detailed level, accountants lack a practical understanding of what tasks data analytics involves and how to implement and carry out a sophisticated data analytics function.
This column aims to shed some light on remedying the latter problem by introducing general knowledge bases that accounting-specific education in data analytics could be rooted in, which has particular relevance for universities and colleges that seek to integrate data analytics into their programs in the next few years. In addition, the column links some specific areas of data analytics-related computer science to a business-oriented data analytics process. In providing these links, examples are shared to explain how various potential accounting-related tasks, including those of tax practitioners, could be served by data analytics tools.
A base of computer science and programming
Organization and structure are two attributes of accounting that draw many individuals to the profession. Data analytics involves adding structure to data to enable effective and efficient decisionmaking. Thus, in theory, accountants should make excellent data analysts. However, it is estimated that nearly 80% of enterprise data is unstructured currently (Stippich and Preber, Data Analytics, p. 7), which means that the large majority of firm-level data is not in a readily available database format.
Thus the data analytics process of today's businesses involves at least two primary challenges: (1) collecting and categorizing voluminous data and (2) analyzing and prioritizing relevant data. Traditional accounting education models are not well-designed at the moment to prepare future practitioners for these two challenges, and, thus, in practice, accountants need additional knowledge and tools to continue to be excellent data analysts. While at the moment organizations seem to be pairing nonaccountant data scientists with accountants, and universities are hiring computer scientists to teach students and conduct research, the long-range goal is to train accountants who are also data scientists.
As a first step, then, practitioners and educators need to continue a recent emphasis on developing a common set of tools for future accountants to acquire at a university or college level. Conceptually, this change does not require any unfamiliar topics; accounting students have traditionally been required to take courses in mathematics, computer science, and...