June asked her mother, who is an accountant, “What are ten challenges that a Certified Public Accountant (CPA) may face with the advent of data analytics?” Please help June.

Accountants who use data analytics face challenges that include an overwhelming amount of data, the potential to misinterpret data and trends, misunderstandings about data analytics and its methods, security issues, and potential difficulties in descriptive, diagnostic, predictive, and prescriptive analytics.

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Data analytics is a discipline that takes numbers and raw data and tries to discover helpful patterns in them in order to answer questions and draw conclusions. Accountants often find the methods and results of data analytics quite helpful, but there are also challenges involved. Let's brainstorm some of the latter.

First, data analytics often uses and provides a large amount of information that can sometimes be overwhelming. It may, in fact, be far more than many accountants need to do their work efficiently. Further, accountants may sometimes fail to understand the methods of data analytics or the categories into which data is grouped. This, too, can lead to confusion and frustration. The risk of misusing data and the risk of breaches in data security are also challenges for accountants and others who interact with the discipline.

Data analytics itself is often divided into four subcategories, each of which can provide some challenges for accountants. Descriptive analytics looks for trends across a period of time. Sometimes, though, these trends may not truly reflect reality or may provide an incomplete picture. This can lead to skewed assumptions and conclusions.

Diagnostic analytics attempts to discover the reasons behind the trends, but hypotheses can sometimes be wrong or insufficient to explain trends, and accountants may find themselves caught up in theories that are not really supported by the numbers.

Predictive analytics, too, provides a set of challenges for those who must try to sort out numbers and trends and predict what might happen in the future. Just a couple of mistakes can lead to a budget that is way out of line or investment predictions that do not play out as planned.

Prescriptive analytics are even more tricky, as accountants use them to advise their clients on financial moves. If the data is off or if the interpretations of the data are wrong, then that advice may also be faulty, leading to financial losses or, at least, less financial gain than expected.

Data analytics, then, presents a set of challenges for accountants. This does not mean, of course, that accountants should not use data analytics or appreciate the benefits the discipline can bring to their work, but they must be wary of making judgments too quickly, of misinterpreting data, and especially of placing too much emphasis on numbers that change quickly.

Last Updated by eNotes Editorial on
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