Data analytics is the process of processing and examining data to uncover useful information and help users make decisions. In auditing, descriptive analysis and diagnostic analysis are the two main types of data analysis that are used.
Using data analytics helps the audit team improve their understanding of the data and examine the entire portfolio. In addition, data visualization helps uncover trends or correlations in the data, allowing the audit team to focus on high-risk areas.
For example, for the risk assessment process related to receivables on the balance sheet, if a preliminary analysis is performed, only the growth or decline over the years can be assessed. But by applying data analytics, the auditor can look at receivables along with the age of the debt over the years, which can assess the magnitude of the increased risk involved. Therefore, the engagement team can recommend the appropriate procedures for examining the item.
Meanwhile, DA is the method of data or information analysis to draw conclusions and facilitate the decision-making process (World Bank Group, 2017).
As a concept, DA primarily encompasses IT functions and applications, from basic business intelligence (BI), reporting, and online analytical processing (OLAP) to multiple modes of advanced analysis used to analyze data. In the exam context, DA involves larger and more complex procedures during the exam process. This requires the use of sophisticated software or advanced statistical tools and techniques. This can include cluster analysis, predictive modeling, data layers, visualizations, and what-if scenarios that enable the use of new strategies to assess large amounts of relevant audit information. The use of analysis tools allows auditors to collect information from internal and external sources as proof in various phases of an examination, e.g., during analytical procedures, testing of controls, risk assessment, and statement-related procedures (Tschakert et al., 2016).
Recent discussions in the audit profession have recognized the importance of DA in audit practices (Vasarhelyi et al., 2015). As stated by Capriotti (2014), it “has the potential to be the most significant shift in how audits are performed since the adoption of paperless audit tools and technologies”. There are at least three main benefits of using DA in an audit, as auditors can take advantage of analytical tools and technology (Gray and Debreceny, 2014; McGinty, 2014). First, DA allows auditors to automate transaction testing, and theoretically, 100% of the population audited can be tested (Liddy, 2014). Second, audit quality can be increased by enabling a better understanding of client processes through the identification and analysis of accounting anomalies (BrownLiburd and Vasarhelyi, 2015; Capriotti, 2014; Whitehouse, 2014). Third, using DA can improve fraud detection in an audit (Earley, 2015).
However, implementing DA in revision is not an easy task. There are requirements such as the need to understand the current scope and limitations of the auditing profession before imagining the role of more complex analytics and DA in audit practice (Appelbaum et al., 2017a, b). Salijeni et al. (2018) indicated that there are several conceptual discussions in the literature about the factors influencing the use of DA in assessment practices. For example, Krahel and Titera (2015) discussed the need for specific accounting and auditing standards related to DA that facilitate the approach, analysis, and presentation of data. In addition, the application of DA in practice can be promoted by appropriate standards with guidelines on questions related to the examination of large amounts of data, e.g., data collection, error response, and auditor competencies. Conducting DA is some of the inhibiting factors in including the DA in external audits. Empirical evidence from interviews with 21 participants conducted by Dagiliene and Klovien _e (2019) found that firm- related or audit clients (such as size, data-driven strategy, and business model) and institutional aspects (such as competition in the audit market, regulatory policies about BD and DA and educational institutions) are important motivating factors for the application of DA exam practice. A questionnaire study by Eilifsen et al. (2020)
documented that auditor found DA audit tools simple and not complex enough to use in the course of an audit. The auditors also recognized that DA can be applied effectively in audit practice when organizations have adequate DA tools, the necessary skills, and the availability of professional support for the use of DA in audit engagements. The auditors also strongly emphasized the importance of integrating customer information systems to enable the use of DA in audits.
Respondents in a study by Salijeni et al. (2018) highlighted the challenges of including the DA in audits, including detecting “false positives” resulting from testing. 100% of the population, costs associated with excessive auditing, over- reliance on analytical specialists, and insufficient guidance on auditing standards.
The benefit of DA in assurance is the ability to use non-financial data (NFD) and external data to better inform audit planning (particularly in risk assessment) and more effectively audit those areas that require judgment, such as valuation or going concerned. Because auditors can develop models that can predict future events, often referred to as predictive analytics, they can better help their clients make strategic decisions about their business. NFD includes data that the company collects internally, such as human resources data, customer data, marketing data, etc. that goes beyond the types of financial statements that auditors normally look at. As pointed out by Alles and Gray (2014, p. 16), “the vast majority of data in dig data is NFD”.
According to Gartner's IT Glossary, the types of data analysis commonly used in financial statements are descriptive and diagnostic: Descriptive analysis is the study of data or content to answer the question "What happened?" and is often characterized by traditional business intelligence and visualizations such as pie charts, bar charts, line charts, tables or generated narratives. Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question
"Why did this happen?" and is characterized by techniques such as drill-down, data discovery, data mining, and correlations.