Big Data analytics and financial reporting quality: qualitative evidence from Canada

Abstract

Purpose

Big Data analytics (BDA) and its implications for the accounting profession continue to be a key issue that requires more research and evaluation. As a result, the purpose of this study is to evaluate the impact of BDA on financial reporting quality, as well as to assess the accounting challenges associated with Big Data. It provides qualitative evidence from Canada.

Design/methodology/approach

This study used a qualitative approach to ascertain the thoughts and perceptions of auditors, financial analysts and accountants at Canadian audit and accounting firms in BDA and its impact on financial reporting quality, using semi-structured interviews. To obtain their consent to participate in the interview, 127 auditors, financial analysts and accountants from Canadian audit and accounting firms were initially approached. The final number of respondents was 41, representing a response rate of 32%.

Findings

The authors’ findings underscored the relevance of Big Data and BDA in affecting financial report quality and revealed that BDA had a significant effect on improving financial reporting quality. Big Data improves accounting reporting and expert judgment by providing professional. In summary, participants agreed that when analytical methods in Big Data are implemented effectively, businesses may possibly achieve a variety of benefits, including customized goods, simplified processes, improved risk assessment process and, finally, increased risk management.

Practical implications

The authors’ findings indicate that BDA may help predict investment returns and risks, estimate future investment opportunities, forecast revenues, detect fraud and susceptibility early and identify economic growth opportunities. As a result, auditors, financial analysts, accountants, investors and other strategic decision-makers should be aware of these findings to make informed choices.

Originality/value

Big Data has become the norm in recent years; accountants and other decision-makers have struggled to analyze massive amounts of data. This limits their capacity to profit from such data even more. Therefore, this study is motivated by the lack of research on Big Data’s influence on financial report quality.

Citation

Saleh, I., Marei, Y., Ayoush, M. and Abu Afifa, M.M. (2023), "Big Data analytics and financial reporting quality: qualitative evidence from Canada", Journal of Financial Reporting and Accounting, Vol. 21 No. 1, pp. 83-104. https://doi.org/10.1108/JFRA-12-2021-0489

1. Introduction

The business climate is constantly changing, and organizations must be informed of good and bad events (Najjar et al., 2022; Momani et al., 2021). Following the 1990s event dubbed “Economia,” worldwide business competitiveness intensified considerably, resulting in the emergence of a variety of new business dynamics. Enterprise systems (and even specific legacy systems) were developed at this stage to handle more transactions and transport data across firms (Janvrin and Watson, 2017). Consequently, firms that deal with large volumes of data should steer clear of random data processing approaches (Wisna, 2013; Warren et al., 2015). The advancement of techniques like pattern recognition and natural language processing has increased the predictive capability of data analytics (Al-Omoush et al., 2021). Due to advancements in data collection and processing technology, the role of managers, accountants and auditors in producing, analyzing and encouraging information use has shifted away from simply providing accurate and reliable information and toward providing a system that can be monitored and improved over time to provide accurate and reliable information (Elliott, 1992; Zadeh et al., 2015; Balios et al., 2020; Abu Afifa et al., 2022). Additionally, accounting has evolved into a discipline capable of adapting to massive volumes of data due to its importance to enterprises and policymakers in acquiring information and data about economic events (Davenport, 2014; Cappa et al., 2021).

Accounting experts have long been critical to several businesses’ strategic goals and decision-making processes. Financial accountants were hired to analyze structured financial statement data to determine the factors that contributed to a business’s performance (Younis, 2020). However, financial accountants will need to go beyond conventional tools to aid in strategic decision-making processes in the era of Big Data. Accounting professionals may be able to find possibilities for development and places for growth by adding enormous amounts of data into their monitoring operations through Big Data Analytics (BDA) (Varma, 2018; Gandomi and Haider, 2015). Similarly, financial statement audits have historically been used to examine structured data (Zhang et al., 2015). When an auditor plans to control substantive testing, he or she is driven by the goal of gathering adequate audit evidence (Yoon et al., 2015). Most auditors concentrate their data analytics efforts on data supplied by client-provided accounting information systems, which are often basic and have inherent structures (Salijeni et al., 2019). To maintain a competitive edge in the assurance services industry, auditors must develop unstructured data analytic capabilities that allow them to apply their problem-solving techniques to considerably bigger data sets (Balios et al., 2020). These continuous changes in the Big Data environment prompted us to investigate the influence of Big Data on financial reporting from both an accounting and auditing standpoint and, additionally, evaluate the accounting issues posed by Big Data. In this article, we will attempt to bridge the gap between the pieces of literature on financial reporting quality and Big Data.

We gathered data for this study using an interview strategy. After evaluating the first set of interviewees, we performed a second set to elicit more information and ensure that we covered as many areas as feasible to meet the study’s goals. Our findings contribute to accountants, auditors and financial analysts’ comprehension of Big Data and its influence on the quality of financial reports. We stated that although financial reports may be strengthened via data analytical tools, the majority of small audit and accounting firms are unable to reap the benefits due to a lack of resources for data analysis.

Our work provides significant additions to the works of literature on financial report quality and Big Data. Historically, research into the quality of financial reports has tended to concentrate on macro-level issues such as company culture (Bhandari et al., 2022), audit quality (Gaynor et al., 2016), system/administration characteristics and regulatory influence (Nirwana and Haliah, 2018). We focused on the qualitative aspects of meaningful financial information and how these traits interact with Big Data as part of our research. To explore the issues inherent in Big Data and accounting, we took two views and partitioned our data accordingly. Our presentation emphasizes the critical nature of comprehending and using Big Data in light of these concerns. When businesses are classified according to their size and experience, more accurate data can be gathered, and Big Data can be better comprehended. We share insights into how accounting in small and inexperienced financial reporting contexts uses analytical tools to improve quality using information from our interview data.

The paper is organized as follows. Section 2 provides an explanation of how institutional theory can be an asset in explaining the financial reporting quality and the adoption of BDA. The literature on Big Data with accounting is then provided. Section 3 outlines the methods used. Section 4 presents an analysis of interviews and discussion. Finally, Section 5 presents the conclusion, and Section 6 provides the implications and limitations of the study.

2. Literature review

2.1 Theoretical framework

We investigate the financial reporting quality and Big Data using institutional theory, a branch of theorists that is widely applied to accounting. By examining this factor, we may obtain a better understanding of how accountants, auditors and financial analysts perceive and respond to changing needs and expectations. We use institutional theory’s similarity between two and dissociation features to explain compliance with difficult accounting standards and the need of BDA. Institutional theory tries to inform organizational decision-making by offering a conceptual lens through which to view organizational activities (Suddaby, 2010). The idea is predicated on the concept that things do not operate alone, but rather are linked by what are referred to as organizational fields, which eventually evolve into societies (Tina Dacin et al., 2002). The validity of the circumstances that restrict and influence conduct is a major issue of institutional theory. As an organization pursues this important resource, it is compelled to develop rituals and myths around its acts to maintain the confidence and permission of its critical constituents (Sellers et al., 2012). Covaleski et al. (2003) stated that the pursuit of efficacy and legitimacy cannot be considered in opposition to one another.

DiMaggio and Powell (1983) distinguish three distinct forms from the three isomorphic pathways. Coercive, mimetic and normative isomorphisms are used to accomplish this. These types of theories justify distinct organizational strategies for development and execution. We explore the accounting usage of a BDA in each of the first two distinct isomorphic systems.

First, whether imposed by external stakeholders such as rival firms, end consumers or the larger society, coercive isomorphism results from external pressures on a company. A business adopts an innovation because it cannot conceive of another method to satisfy critical stakeholders (DiMaggio and Powell, 1983). As a consequence, businesses may make symbolic, surface-level efforts to please these groups (Verbruggen et al., 2011). Firms embrace innovations when compelled to do so to retain perceptions of legitimacy. Therefore, the business is compelled to use its current data collecting and processing technologies to fulfil the expectations and requirements of its significant stakeholders. Second, mimetic isomorphism refers to businesses that desire competitive advantages by replicating their rivals’ information technology strategies. Due to coercive pressure from financial regulators (Mizruchi and Fein, 1999), shareholders and software companies, there would be pressure to replicate the usage of BDA. Bhimani et al. (2016) stated isomorphism of norms arises when they are transferred from one institution to another, most often as a consequence of professional demands. The mimetic isomorphism is predicated on the idea of low cost and low risk. Following a state of transition, the borrowed company mimics the invention of another organization that has previously achieved success via a comparable innovation (Mizruchi and Fein, 1999).

In general, early adopters are assumed to be risk-averse (Tan, 2001). To reduce costs or create supernormal profits, these organizations leverage innovations that capture client attention and enhance their particular companies’ demand curves by doing an activity that their rivals are unaware of or unwilling to perform. Innovative businesses are often those defending market positions (Chandy and Tellis, 2000; Rice et al., 2000), with the resources and scale to develop a portfolio of low-, moderate- and high-risk methods. In certain circumstances, innovation happens when enterprises compete for market leadership; they use innovative strategies to capture new markets ahead of more profitable or larger rivals (O’Connor and DeMartino, 2006).

The institutional theory asserts that although organizations construct formal mechanisms, their actual behavior may deviate significantly from these that do not have a framework. As a result, separate institutional frameworks or methodologies emerge. As a consequence, Coyne et al. (2018) report that transforming Big Data into useful information has grown more challenging, despite accountants’ familiarity with the necessary techniques. Accounting professionals who assist in the governance of Big Data will face a shortage as a result. According to Palem (2014), companies acknowledge the importance of Big Data but are uncertain about its purpose. Big Data Solution providers often bridge the gap between customer needs and Big Data requirements.

2.2 Big Data and accounting

BDA and machine learning become prominent subjects in the small company and academic sectors as a result of new technologies such as social, mobile and cloud (SoMoClo) that create massive amounts of data (Cisco, 2015; Jin-Lung et al., 2019). They enable businesses and people to access, gather and analyze massive volumes of data from a number of sources. Accounting has lately started to handle the issues of Big Data and data analytics. In the literature, only limited amounts of articles have tackled Big Data. However, Big Data is more than a collection of data; Big Data transcends size. In a culture-based, experience-based, utility-based and expectation-based society, there is a strong focus on empirical knowledge. As a consequence, individuals acquire automatic access to resources while remaining oblivious of them (Jariwala et al., 2015). They gather all accessible information rather than concentrating just on one media. As a consequence, data analytics may be carried out more quickly using existing infrastructure. The importance of social media is undeniable (Chen et al., 2012; Earley, 2015). In their investigation, Warren et al. (2015) recognized Big Data as a viable solution. As a result, accounting procedures and the profession as a whole should experience significant transformations. On the other side, the reliability and use of financial accounting data and, therefore, the level of transparency stakeholder decision-making and corporate reporting will be facilitated. Improved regulations will be necessary to ensure the profession’s sustained advancement and to stay economically viable in today’s economy (Watson, 2014; Warren et al., 2015).

To effectively use Big Data, accountants must track and record data; report to stakeholders; and control both people and assets for the purposes of safeguarding assets, enacting processes and providing accurate information. Because Big Data is so large, traditional techniques and databases cannot be applied to analyzing them (Warren et al., 2015). Vasarhelyi et al. (2015) assert that Big Data profoundly alters our notion of “data.” For example, in a real-time setting, entities may deliver information in a timely manner. As a consequence, accountants need be conversant with Big Data analysis to perform their duties more effectively. According to Bhimani and Willcocks (2014), accounting practice is ready for digitization; nonetheless, they express worry that the way accountants perceive data is difficult to convert into practice and that data might provide insights if only they were “tortured enough.” Payne (2014) believes that continuously digitizing accounting is difficult for a variety of reasons, including information leakage, communication failure and data analytics. Quattrone (2016), on the other hand, contends that accounting must respond to change technologies to stay competitive.

2.3 Quality of financial reporting

The surge in accounting scandals globally since the early 21st century demonstrates flaws in financial reporting quality. As a consequence, financial reporting’s quality and worth are decided and the global need for a comprehensive definition of financial reporting quality has increased. It is critical to provide high-quality financial reporting to enable consumers to make educated investment choices and to increase market efficiency. Financial reporting quality, as defined by the Financial Accounting Standards Board (FASB), the International Accounting Standards Board (IASB), the International Financial Reporting Standards (IFRS) and Accounting Standards for Private Enterprises Canada, provides reliable and accurate information regarding an entity’s financial position and performance. Different studies looked at financial qualities from different point of view (Beest et al., 2009; Tariverdi et al., 2012; Tang et al., 2016; Huang et al., 2012). For example, different measuring approaches have been used to evaluate the quality of financial reporting. Several of them include the following:

• accrual models (Jones, 1991);

• accounting-based models (Choi et al., 1997);

• report critical analysis (Beretta and Bozzolan, 2004); and

• qualitative attributes models (Beest et al., 2009).

In response to the need for improvement in existing model, the International Accounting Standards developed a conceptual framework for financial reporting. As stated in the framework, the primary points are the purposes of financial reporting and the criteria of high-quality financial reporting. It said that adhering to the financial reporting goals and qualitative features is a vital component of attaining excellent financial reporting. Qualitative attributes are described as financial information that fits the framework’s requirements for decision usefulness. These characteristics include relevance, accurate, comparability, comprehension, verifiability and timeliness. To assess financial reporting quality, a model based on qualitative features has been developed (Beest et al., 2009).

2.4 Relationship between quality of financial reporting and Big Data

When we established the link and elucidated its dynamics, we relied on the work of a variety of organizations, including IFRS and previous research inquiries. As a general principle, we would want to align our framework with the qualitative qualities of significant financial data. In this context, we addressed the following question: “How does an end user evaluate the quality of a company’s reporting?” A framework is a great place to start when building a solution to this problem. It should be based on the FASB’s Concepts Statements, which contain the most current thinking on the aims and features of quality financial reporting.

According to the most recent framework for assessing financial reporting quality, the 2018 framework is the best because it uses a methodology that analyzes and quantifies the amount of decision-useful information in financial reports by operationalizing its qualitative characteristics (IFRS, 2018). Jonas and Blanchet (2000) used a framework approach to establish a standard for evaluating the quality of financial reporting. They prepared questions on the qualitative aspects of financial reporting following the FASB and IASB standards. McDaniel et al. (2002), Lee et al. (2002) and Beest et al. (2009) all used an initial framework, but their financial reports share comparable qualitative features. Beest et al. (2009) operationalized the qualitative qualities using the 2008 IASB Exposure Draft (ED), rather than the operationalizations used by McDaniel et al. (2002) and Lee et al. (2002) based on FASB and IASB. However, the IFRS framework benefits from explicitly proving financial reporting quality because it encompasses financial and nonfinancial data.

It is essential that financial information is relevant and represents what it represents with accuracy. Having comparable, verifiable, timely and understandable financial information is more helpful (IFRS, 2018). Consequently, the data generated throughout the financial reporting process is critical for all stakeholders. When it comes to managing and collecting the data, the debate has been more philosophical than procedural or process-oriented. With the introduction of Big Data, which refers to a large volume, a high rate of change and a diverse collection of information assets that need unique processing approaches to improve decision-making, insight discovery and process optimization (Warren et al., 2015; Zhang, 2015). Big Data is a term that encompasses a variety of complicated concepts and meanings. According to Watson (2014), Big Data refers to data that is enormous in volume, changes rapidly and is available in various forms. Likewise, Davis (2014) argues that Big Data consists of large data sets that are updated more often and take on a variety of forms and content. Abbasi et al. (2016) provide a counterargument, suggesting that Big Data is unique from conventional four-dimensional data structures or the Four Vs’, meaning velocity, veracity, volume and variety.

Additionally, several definitions emphasize the data volume’s increasing growth rate, which is often stated in petabytes or exabytes and is used by decision-makers to make strategic decisions (Akter et al., 2016). However, one challenge comes with the data velocity, which refers to the rate at which data is obtained, updated and evaluated and the rate at which the data value becomes obsolete (Davis, 2014; George et al., 2016). Another challenge is variety, which refers to a vast range of organized and unstructured data sources in the context of Big Data. Text, music, photos, video, social media and graphics are examples of this (Constantiou and Kallinikos, 2015; George et al., 2016). When it comes to enhancing overall corporate efficiency and enabling real-time actions and intraday decision-making, the “newness” of acquired data and the analytical power of these data streams are critical (White, 2011).

Accounting and audit firms are significantly impacted by Big Data (Coyne et al., 2018). Allowing them to assume strategic positions inside their organizations enables them to evolve from decision-makers to business partners. Faye (2016) asserts that Big Data is the future of many businesses and will alter the responsibilities of accountants and financial analysts. Additionally, Richins et al. (2017) predict that around 94% of accounting and auditing occupations will be automated by 2025, and in this situation, mastering data analytics skills may assist accountants to add value to their professions (Stryk, 2015). Professional accountants and financial analysts must strive to remove any distinctions between information technology and business (Gamagea, 2016). According to McKinney et al. (2017), the near future presents opportunities and opportunities for those with accounting backgrounds who also possess technological and statistical capabilities for organizing and analyzing enormous volumes of data. These individuals will get to the top of the pay scale.

As a result, accountants must maximize their potential and develop their capacity to comprehend Big Data to enhance the quality of financial information and provide value to businesses (Deniswara et al., 2020). According to Cohn’s (2020) study, Big Data is heavily integrated into business programs run by finance and accounting specialists. As a result, they anticipated significant changes in their businesses in 2020. The onus is now on accountants to have the necessary skills to keep pace with technology improvements and shifting behavioral trends among crucial business partners. Data visualization skills are rapidly gaining popularity and are crucial for management accountants. Indeed, they have evolved into a necessary component of many accountants’ duties (Deniswara et al., 2020). This is how management accountants connect data scientists and executives. BDA will impact financial accounting by affecting the collection, storage and administration of data and the generation of financial statements (Schneider et al., 2015).

Numerous technologies are expected to reshape the accounting profession, including Big Data, artificial intelligence, cloud computing, machine learning, AI algorithms, data analytics, blockchain, robotic process automation, new business networks, e-payment systems, digital services, cybersecurity, virtual reality and social media (Simone, 2020). Technological improvements are expected to decimate the accounting profession, placing increased emphasis on accounting professionals to learn new skills and use data analytics tools. Additionally, via the use of Big Data and algorithms, these technological breakthroughs allow fraud detection. The researchers concluded that BDA is critical for accounting as a result of their results. It contributes to the field by providing information that aids in improving the quality of financial information, maintaining accurate and complete accounting records, performing asset evaluations, ensuring the transparency and completeness of financial reports, advancing and using accounting principles.

According to Sun et al. (2018), BDA works by extracting relevant information from a vast data set to help decision-making and analyze and enhance business operations. As a result, BDA seems to be capable of recording causal and related actions, financial accounting and inheritance reporting in real-time. These are dependent on comprehensive data and various tiers of summary, aggregate and reporting (Griffin and Wright, 2015). According to the research, firms find Big Data opportunities via analytical marketing, communications, retail and review approaches (Salijeni et al., 2019). Raj et al. (2015) argue that several data analysis tools and approaches are accessible. Hadoop (a widely used technology), MapReduce, GridGain, HPCC, Storm, Sap Hana and Cassandra are among them. It is a free and open-source application or software platform built in Java that facilitates distributed data storing and processing. For example, data may be stored on several devices, with processing data split among them to speed up the outcome of the processing. According to Philip and Zhang (2014), cloud computing repositories may be used to store massive amounts of data that can be converted into meaningful information. Accounting data users have unique expectations that must be met. These characteristics include a high degree of comprehension, reliability, pertinence, comparability and the quality of information that may influence report consumers’ decisions. Siriyama and Albarqi (2017) define quality as the features of accounting information that fulfil the previously stated criteria. These qualities are included in financial documents and reports and are used to measure decision-making ability, financial failure prediction and the degree to which accounting information is of high quality.

2.5 Big Data in Canada

In the present age of Big Data, a diverse range of diverse data sources is capable of readily creating and collecting huge volumes of important data (Salijeni et al., 2019). Recently, in Canada, every government body, researcher or institution that publishes and makes publicly available data has been urged to join open data projects (Teimourzadeh et al., 2022; Shirazi and Mohammadi, 2019). Canadians’ business practices are increasingly moving away from traditional data collection and processing methods (Aversa et al., 2018); as a result, economic activity can be forecast more accurately and the probability of amended business and consumption data releases can be analyzed more thoroughly by examining these timely electronic data (Swanson and Xiong, 2018).

In Canada, Big Data consists of several data layers (Hinge et al., 2021). The first step toward realizing the full potential of Big Data is to integrate different data sets and make them simply and rapidly accessible. Cloud computing facilitated the development of data centers capable of storing vast amounts of data in a centralized place. Numerous efforts merged data sets to facilitate access to large amounts of data. The governments of Ontario and Canada have partnered with IBM and a consortium of seven schools to launch a $210m research program in an Ontario data center aimed at assisting university and economics scholars in making better use of Big Data (Armah, 2013). A distinct venture was a 2010 agreement, which offered professors and other interested parties access to 170 billion archived tweets for the aim of conducting research and projecting stock market movement. Another example, the Government of Canada established a Communications Research Centre. Each of these initiatives is designed to educate and engage Canadian companies on the advantages of Big Data (Delanoy and Kasztelnik, 2020). As a consequence, more businesses and organizations are using Big Data. Almost all universities and colleges in Canada run programs focused on Big Data, and all business and accounting school programs include courses in Big Data due to the high demand from the majority of Canadian businesses. For example, Delanoy and Kasztelnik (2020) investigate social media and Big Data in Canada and the various available support for BDA to make reasonable business decisions. Kuo et al. (2014) analyze the features of Big Data in health care, as well as the challenges and potential alternatives associated with BDA in health care in Canada.

According to certified public accountant (CPA) Canada believes that artificial intelligence will become so pervasive in Canada that, although it will not completely replace accountants, it will profoundly transform how accountants operate in the nation. As a result, accounting and auditing professionals must acquire new skills, stay adaptive, agile and creative to maintain their competitive edge and assist their organizations in navigating a disruptive and revolutionary technology environment. When CPAs serve as companies, they may use machine learning to increase productivity and capitalize on Big Data opportunities. Austin (2018) discussed the solutions to frequent issues encountered while combining heterogeneous data sets from Big Data in Canada.

3. Methodology

We use semi-structured interviews to determine how and which questions impact the quality of financial reporting in the Big Data area. To develop our questions, we hypothesized that analysis techniques based on Big Data might significantly enhance financial statement quality. To develop the interview questions, we wanted to explain our perspective via a review of the extant literature on Big Data and its use in private enterprises. The “Interpretive Description” arose from the need to generate a better understanding of Big Data and BDA in the quality of financial reporting (Schwartz-Shea and Yanow, 2020).

We considered the amount of time the person or group needed to submit the information while developing the open-questions. We wanted to conduct interviews that lasted between 30 to 45 min. We believed it was critical to provide an initial set of open-questions to all interviewees. This procedure aided the responder in reducing the amount of time necessary (s). While developing the open-questions, we thought that the range of experience among our possible respondents justified the use of many questions. Two interviews have been conducted; the first phase began in January 2020 and concluded in August 2020 and comprised of semi-structured interviews with the database’s initial twenty individuals. The second round of the interviews, which ran from January to April 2021, contained semi-structured interviews, video evidence and paper records. The second round of data gathering comprised additional interviews to enhance, expand and confirm the initial session’s findings. We kept the personalization of questions and interviews to a minimum. We presented a statement outlining the purpose of these meetings prior to interviewing any of the auditors. The objective stated that all these interviews were conducted to ascertain the thoughts and perceptions of auditors, financial analysts and accountants at Canadian audit and accounting firms in BDA and its impact on financial reporting quality. Our strategy incorporated strategies used in past research (Marginson, 2004) to guarantee that the data obtained was valid and reliable.

We made our first contact by e-mail and telephone, outlining the goals of the research and communicating its outputs and providing a confidentiality assurance. We collected data through our interviews, follow-up e-mails and follow-up telephone calls. Following up was done manually. After transcribed data were extracted, it was extracted into manageable sets of information, themes were identified, information was systematically presented, meanings were described, and regularities, patterns and explanations were noted. To obtain their consent to participate in the interview, 127 auditors, financial analysts and accountants from Canadian audit and accounting firms were initially approached.

The final number of respondents was 41 (see Table 1), representing a response rate of 32%. In addition, to consider the challenges associated with Big Data and accounting, we adopted two perspectives and divided our data into those perspectives — size at multiple levels and experience at different levels. Two groups of participants were formed. We classify our participants based on their job title, work experience and firm size. Our discussion focuses on understanding and using Big Data in light of these factors. We provide insights into how accounting in small and inexperienced financial reporting environments uses analytical tools to enhance quality using information from our interview data. When firms are grouped according to size and experience, better information can be collected, and Big Data can be better understood.

4. Findings and discussions

4.1 Big Data and Big Data analytics in financial reporting quality

According to prior studies, four significant characteristics serve as proxy metrics for financial reporting quality: relevance, comparability, understandability and faithful representation (FASB, 2010; Abu Afifa and Saleh, 2021, 2022). Additionally, these features aid in evaluating the quality of accounting data, making decisions and financial forecasting failure (Simić and Ivanović, 2015).

Reports should be relevant because they may influence users’ choices and have a significant effect on their decision-making. According to participants from Canadian audit and accounting firms, audits may adjust audit approaches and offer a higher-quality audit by evaluating client data early in the audit process using Big Data and related technologies. These pertinent data provide a dynamic, forward-looking approach for detecting abnormalities, patterns, connections and variations that might alert auditors to possible risks.

Businesses can possibly accomplish several advantages when analytical approaches are applied properly to Big Data, such as personalized offerings, optimized processes, and better risk management. Data can help firms base their decisions on actual information and facts, rather than guesswork and assumptions. Besides, the sheer volume and breadth of data-driven opportunities is progressively altering the corporate landscape and creating potential for disruptive new business models. [P7]

Big Data and data analytics will have a wide-ranging impact on accounting, influencing how businesses are operated as well as how financial statements are created and audited. [P14]

New data capabilities enable the accounting profession to significantly improve decision-making across organizations. There are several instances of the use of Big Data in the profession to provide new insights into organizations, to focus effort on areas of highest risk, and to enhance prediction. [….] This can assist accountants in providing more certainty over financial statements, improving their financial resource management, and increasing the decision assistance that they can provide to business operations. [P21]

According to McDaniel et al. (2002) and Beest et al. (2009), significance may be quantified using predictive and confirmatory values. Predictive is capable of forecasting future cash flows and profits. Moreover, according to Jonas and Blanchet (2000), the confirmatory value provides comments on prior transactions or events in the annual report that might assist consumers to confirm or adjust their expectations for the future annual report. Accounting data is deemed significant by participants when compared to market share return. According to them, financial measures such as operating income or net income indicate a firm’s success. Using appropriate data while evaluating operational and financial choices is often critical, as it ensures the quality of financial reports.

Big Data is the latest wave that is sweeping through business processes. Businesses who can use the scale, range, and pace of Big Data can make smarter decisions, cut operating costs, and keep up with changing consumer demands. [P10]

Since Big Data makes use of technology and artificial intelligence, data can be processed in greater quantities and at a faster rate to provide useful information (both financial and nonfinancial) to users. [P4]

[…] Data also allows accountants to expand their function into a much broader guardianship of data throughout the entire organization. [P32]

The corporation can analyze its opportunities, risks and possible future scenarios (Jonas and Blanchet, 2000). The interviewees agreed that the vast majority of companies embrace all levels of risk evaluation, regardless of how basic or sophisticated they are. The success of their output may be determined by examining the observational data and determining if it is as relevant as the judgments that guide their selections.

Accountants are increasingly utilizing some of Big Data’s features. Businesses may now access a degree of real-world proof previously unattainable through the use of Big Data. This may assist them in reducing their dependence on assumptions and guessing, as well as providing several chances to enhance firm decisions concerning consumers, suppliers, staff, strategy, and risk. [P35]

Accountants in firms have several options to leverage various data sources and new analytics technologies. These can assist to enhance forecasting by relying on non-financial data or utilizing more real-time data. Similarly, connecting non-financial and financial data might allow for a more in-depth investigation of cost causes. [P19]

Despite this, they acknowledge that having too much data can be problematic because of the conflicting information from many sources and the large quantity of data that must be categorized to be helpful. According to our assessment, this may have something to do with the resources available to accounting and auditing firms. Typically, the smaller the firm, the fewer people it uses and the fewer IT programs required to do quicker data analysis. Our conclusion is consistent with Warren et al. (2015) because they suggested that accounting has become more reliant on Big Data, despite the availability of new data kinds. Big Data was used to make videos, audio recordings and text files to enhance management accounting, financial accounting and financial reporting methods. Management undergoes substantial changes as a consequence of the usage of Big Data. As a result of Big Data, accounting information is more carefully filtered, resulting in more openness and more informed decision-making.

Accounting duties are data-driven. As a result, advancements in the capacity to gather, process, store, analyze, visualize, and exchange data will be especially relevant to how accountants perform their jobs. [P41]

[…] Big Data may aid in the deep study of controls and operational processes, exposing flaws or pressure spots that may be addressed. Furthermore, accountants in advising roles might utilize these technologies to assist organizations with organization strategy or operations. [P23]

Comparability is determined by evaluating how users can identify similarities and differences between two sets of economic events. Organizations must consider outcomes that are alike and those that are unlike in the same manner (Mbobo and Ekpo, 2016). The level of comparability indicates the consistency of accounting rules and processes and the quality of financial reporting among competing organizations. There was broad consensus among the participants that Big Data allows for quick comparisons of businesses in the same industry and helps increase accuracy when comparing companies.

Through the detailed information offered by Big Data analytics, Big Data analytics increases the comparability of the company’s sectors, the company’s comparison for more than one financial period, and between similar companies. [P5]

Furthermore, participants affirm that Big Data increases the comparability of businesses of similar size, industries and complexity in corporate accounting, leading to improved reporting quality.

Big Data analytics contributes to increase the cognitive content of financial information. [….] This contributes to increase the comparability characteristic of the information provided by accountants to users, both external and internal users. [P13]

The information must be classified, described and handled concisely to facilitate data comprehension, enabling the user to comprehend its significance. The manner in which financial reports, notes and any supplementary information are presented is often used to determine their understanding (IFRS, 2018). Participants stated that while using BDA, data collection, classification and code content were simple to interpret. The data will become more straightforward and more transparent to all consumers of financial reports.

Big Data analytics leads to the creation of financial reports, as well as improve risk prediction. Additionally, present concealed information in financial reports to lessen information asymmetry and hence increase the validity of financial reporting. [P1]

As it examines internal information such as talks, Big Data analytics gives information that makes accounting information verifiable and neutral, has faithful representation, and is reasonably devoid of errors and bias. This boosts the financial position’s credibility. [P36]

Big Data analytics has a significant impact on the future and accuracy of financial reports, as well as the evolution of accepted accounting principles, particularly with regard to the disclosure of “off-balance-sheet assets” and fair value accounting. [….] This will work on increasing the relevancy of financial information. [P22]

Detailed Big Data aids in understanding the opportunities and constraints of investment options, which means that more detailed data contributes to take a good investment option. [P2]

Big Data analytics leads to improve understanding and analytics of the substance of information contained in financial reports, reveal ambiguous information, offer a more accurate picture of the organization, and enhance understanding of other information contained in reports. [P8]

Big Data analytics focuses on improving the understanding of the company’s strategic performance, then on improving the understanding of the company’s various operations, and finally on improving the understanding of the company’s overall performance, providing valuable information to decision-making. [P33]

Big Data symbolizes the future and evolved components of the information business and value creation with the goal of encouraging economic development, stimulating growth, rationalizing decision-making, boosting productivity, and product quality. [….] A better opinion can be formed by depending on extensive information about the company, which leads to a better understanding of the company’s success or failure. [P41]

Nonetheless, some analysts claim that small enterprises and external audit firms lack the equipment required to gather and categorize data, resulting in occasional disparities between financial reports. Both technical and economic elements of audit quality are the auditors’ responsibility within a firm, which is valid for both big and small audit companies.

[…] Internal and external auditors have been at the forefront of accounting’s use of Big Data. The capacity to analyze complete data sets — in some cases billions of transactions in a ledger – is transforming traditional sampling-based audit procedures. [….] While auditors will continue to perform extensive work on smaller samples of data, audit analytics allows them to spot anomalies and exceptions and focus on the areas of highest risk. They can also utilize a variety of analytics tools to visualize the data, combine financial and non-financial data, and compare anticipated results to the real world. [P36]

This result aligns with previous studies; according to Gul et al. (2013), audit quality may be impacted by audit features. Their research discovered that auditor quality must be examined and comprehended at the individual auditor level. Therefore, considering how Big Data helps auditors better comprehend transactions and reports and hone their abilities.

Annual reports must be accurate in describing the economic phenomena they are meant to depict. Additionally, they must be precise, unbiased and devoid of substantial flaws. The annual report must appropriately represent the macroeconomic elements described in the data by being thorough, objective and devoid of substantial inaccuracies. According to Bradshaw et al. (2001), Rezaee et al. (2003), Vander Bauwhede et al. (2003) and Cohen et al. (2008), financial statements alone cannot accurately depict an entity’s nature because natural economic processes must be grasped to offer an accurate picture. Proper representation is often evaluated by three criteria: irrefutability, objectivity and a complete report. Most participants, especially auditors, agree that BDA will contribute to data integrity improvement. By preparing and purifying data to fulfil specific criteria, BDA ensures the structure and substance of the data remain intact. This finding is consistent with earlier research indicating that an auditor’s usage and assessment of Big Data visualizations are impacted by their processing mode of choice, whether intuitive or deliberate. Additionally, after analyzing conventional audit evidence, auditors who examine Big Data visualizations that feature patterns conflicting with management claims are more worried about possible misstatements, according to Rose et al. (2017) results.

Big Data analytics has a positive impact on the company by increasing the accuracy of forecasting future earnings and dangers, enhancing forecasting of future growth possibilities, increasing the accuracy of future sales forecasting, anticipating financial fraud, detecting weaknesses and strengths early, and enhancing financial reports’ evaluation capability. As a result, performance evaluation can be improved. [P17]

Big Data and time-series analytics can be used by management to forecast net income, stock prices, fair value assessments, risk assessment, and financial fraud detection. […] Appropriate data is more important to take appropriate decisions in the company. Hence, more data with using good data analytics tools help accountants to provide financial and nonfinancial information to managers to take appropriate decisions. [P21]

Big Data analytics tools aim to assess and transform data into usable information. Thus, Big Data analytics tools make data (both financial and nonfinancial) predictable. This, in turn, helps to increase the relevancy of accounting information. [P38]

4.2 Big Data and Big Data analytics challenges in accounting

A massive quantity of data is created due to technological advances such as SoMoCLo (Cisco, 2015), which makes Big Data and machine learning a prominent issue (Chen et al., 2012). Consequently, corporations and others may now gather, aggregate and analyze massive volumes of data. Participants from Canadian audit and accounting firms believe that these data are derived from an organization’s databases, generally regarded as clean and trustworthy, for example, time series analysis of historical financial data (O’Brien, 2015). Additionally, dark data may be retrieved from various sources, including social networks, operations and maintenance, the public and user-provided cloud storage (Lee, 2017).

Auditors’ emphasis on Big Data and data analytics will major affect accounting, business operations and the production and scrutiny of financial statements. All participants from Canadian audit and accounting firms agreed that companies might profit from the proper use of Big Data analytical tools, including increased risk control (Zhang et al., 2015), customized solutions and simplified processes (Cong and Du, 2021). Making business judgments based on facts rather than predictions and assumptions helps companies make more informed business decisions. Additionally, the abundance of data-driven possibilities reshapes the corporate landscape and stimulates the development of innovative business models.

[…] Accountants must improve their data analysis abilities in order to cope with huge amounts of available data, particularly automatically mined data. [P10]

Traditional software and database systems cannot analyze this massive, unstructured and concurrently unorganized data. While financial information on the substance and timing of transactions is required for comparative purposes, the disclosure should provide adequate detail by providing less condensed financial information. A few accountants stressed the need of having accounting systems that can manage a large volume of organized and unstructured data on clients and market trends in a non-linear way during a start-up’s early phases.

The availability of Big Data dramatically alters the accounting process. Because Big Data is largely unstructured data collected from audio, video, and images, traditional accounting software and database systems cannot adequately analyze and generate financial reports. [P36]

In addition, our finding indicates that the method for deriving financial statements from static GAAP-based data is out of step with Big Data improvements. As a result, Canadian firms will be replaced with raw data that end users may extract and evaluate dynamically, which may replace accountants’ jobs. This contradicts the finding of prior studies; Cao et al. (2022) said that accountants have experience dealing with organized data sets, which assists them in transitioning to unstructured data and are informed about business ideas. BDA, in this case, enhances rather than replaces the talents and knowledge of accountants. In addition, Earley (2015) and Brown-Liburd et al. (2015) cautioned that non-audit enterprises might provide audit services in the future, creating a danger to public accounting firms. Non-accountants will be able to acquire audit evidence and perform financial statement audits more rapidly and efficiently by using data analytics to Big Data.

Big Data has the potential to drastically alter accounting standards. Current accounting standards are relics of a time when data transmission prices were high and data collecting speeds were sluggish; nevertheless, such working conditions are no longer applicable. [.…] Because accounting standards in the age of Big Data will place greater duty on users to demand available data, it is critical that future accounting standards balance the requirement for transparency with the requirement for sensitive data security. [P22]

Big Data alters accounting perspectives by enabling real-time accounting and removing the need for a periodic basis. [….] Fair value accounting is one area where Big Data might have a significant impact. Data service companies specializing in collecting and assessing specific data from diverse sources may arise, allowing Big Data relevant to the fair value of assets and liabilities to offset subjective assumptions in fair value calculations. [P19]

Additionally, participants pointed out that the accounting process must be modified to include unstructured customer events that impact economic value production throughout data collection and financial reporting.

Managerial accountants have a dilemma as a result of Big Data. They must learn more about cyber and information security, because of growing worries that commercially sensitive data in the cloud is exposed to cyber-attack. [….] Traditional management may be revolutionized by integrating sophisticated monitoring and control systems using Big Data analytics tools. However, adopting sophisticated monitoring and control systems using Big Data analytics comes at a considerable cost and effort. [P16]

While the average financial accountant will use small datasets, the management accountant may be tasked with looking at much larger datasets to spot trends, identify opportunities, and support strategic decisions; this will necessitate substantial data analytic knowledge as well as the support of additional data analysis tools beyond basic spreadsheets. It may also necessitate a significant shift in business attitude to shift from human experience to data-driven analytics in order to make judgments. [P14]

This aligns with prior studies; according to an earlier study (Patatoukas, 2012), using Big Data may be tremendously beneficial for many enterprises. By contrast, adopting this strategy blindly might be fatal if decision-makers lack a fundamental understanding of the company. Furthermore, to comprehend business, the accountant and auditor must comprehend unstructured data.

5. Conclusion

This study contributes to the current body of knowledge on BDA and financial reporting by investigating the efficacy of financial reports and their link to Big Data from the viewpoints of auditors, financial analysts and accountants in Canadian audit and accounting firms. The data gathering process consisted of two phases: the first took place between January 2020 and August 2020, and the second took place between January 2021 and April 2021. Across the sample, the findings indicate that BDA is critical for improving financial report quality and has a significant influence on increasing financial report quality. According to the study’s results, Big Data’s capacity to give correct information swiftly can potentially influence the world of financial accounting and reporting substantially. The findings corroborate previous research, such as (Chen et al., 2012). As a result, Big Data differs from conventional data in its large volume, quick pace of change, variety and authenticity. This term has been used lately to allude to massive volumes of data that need sophisticated management strategies (Varma, 2018). Accounting and auditing professionals have several chances to expand their skill sets via Big Data (Alles, 2015).

Additionally, the findings emphasize the critical role of Big Data in predicting investment returns and risk, projecting future investment possibilities, forecasting income, identifying fraud and vulnerabilities early, investigating economic prospects and increasing performance evaluations. Additionally, the findings reveal that all participants believe that relevance, comparability, truthful representation and understandability contribute to improving accounting data quality, decision-making and financial failure prediction. Consequently, analysts, investors and other strategic decision-makers should be aware of the results to make the best judgments. Additionally, this research demonstrates many advantages associated with Big Data for financial reporting and businesses. It can assist institutions in gaining a significant competitive edge, add value, rationalize judgments, develop a holistic vision for the organization, develop futuristic strategies, implement a developed business plan, improve the quality of information real-time availability and enhance support services. Additionally, the quick and exact nature of Big Data enables more efficient and precise reporting, improves risk management techniques, discovers cost-cutting possibilities and increases profitability. According to a study (Rembert, 2020; Salijeni et al., 2019; Appelbaum et al., 2018; Sun et al., 2018; Balios, 2021), BDA may be used to improve the quality of financial reporting and accounting judgement.

6. Implications and limitations of the study

This study underlines the importance of Big Data, specifically BDA, in improving the quality of financial reports by making available accurate, enormous data that auditors and accountants can verify. Because audit procedures and substantive testing are frequently linked in the area of Big Data, and because financial reporting quality and audit are frequently linked in audit procedures and substantive testing, we conclude that audit clients can achieve their objectives with less testing by leveraging Big Data. Additionally, although many of the regular operations presently performed by accountants may be automated, new Big Data analytical tools will not entirely replace the need for accountants. In addition, by bridging the gap in the literature on financial reporting quality and Big Data, we hope to provide a map for future financial accounting and data analysis research in the Big Data area.

Numerous software systems exist on the market that can analyze enormous amounts of data, but smaller businesses may not be able to buy them owing to restricted resources. Our research demonstrates the need to establish software systems for small businesses and complex software for bigger businesses.

While academic research has not directly examined the relationship between Big Data and financial reporting quality, we examined financial reporting quality factors from an objective financial reporting perspective. However, additional factors affect the quality of financial reports, such as corporate culture and earnings management. Future studies may be required and provide more significant insights into other elements of reporting quality and Big Data.

Our analysis was confined to audit and accounting firms in Greater Toronto, Canada. The conclusion may not be relevant in other environments; nevertheless, future studies might investigate and compare our findings to see whether they are similar.

Table 1.

List of interview participants

Code	Participant	Audit/Accounting Firm	Position (s)	Experience (s)
P1	Partner– audit methodology	Big Four	Audit manager	10 years
P2	Partner– audit methodology	Big Four	Audit manager	15 years
P3	Partner– audit methodology	Mid-tier audit firm	Audit manager	15 years
P4	Partner– audit assurance	Big Four	Auditor	15 years
P5	Partner– audit assurance	Big Four	Auditor	10 years
P6	Partner– audit assurance	Mid-tier audit firm	Auditor	7 years
P7	Partner– audit assurance	Mid-tier audit firm	Auditor	10 years
P8	Partner– audit risk analytics	Mid-tier audit firm	Auditor	8 years
P9	Partner– audit risk analytics	Big Four	Audit manager	10 years
P10	Partner– audit risk analytics	Mid-tier audit firm	Audit manager	5 years
P11	Partner– data assurance	Big Four	Auditor	11 years
P12	Partner– data assurance	Big Four	Auditor	13 years
P13	Partner– data assurance	Big Four	Audit manager	7 years
P14	Partner– data assurance	Mid-tier audit firm	Audit manager	5 years
P15	Partner– data assurance	Mid-tier audit firm	Audit manager	5 years
P16	Partner– data assurance	Mid-tier audit firm	Audit manager	8 years
P17	Data Analytics auditor	Big Four	Auditor	10 years
P18	Data Analytics auditor	Big Four	Auditor	12 years
P19	Data Analytics auditor	Mid-tier audit firm	Audit manager	8 years
P20	Data Analytics auditor	Mid-tier audit firm	Audit manager	9 years
P21	Financial analyst	Big Four	Expert in Big Data	5 years
P22	Financial analyst	Big Four	Expert in Big Data	20 years
P23	Financial analyst	Accounting Firm	Expert in Big Data	7 years
P24	Financial analyst	Accounting Firm	Expert in Big Data	9 years
P25	Financial analyst	Mid-tier audit firm	Expert in Big Data	5 years
P26	Accountant	Accounting Firm	Senior Accountant expert in Big Data	10 years
P27	Accountant	Accounting Firm	Senior Accountant expert in Big Data	12 years
P28	Accountant	Accounting Firm	Senior Accountant expert in Big Data	6 years
P29	Accountant	Accounting Firm	Senior Accountant expert in Big Data	5 years
P30	Accountant	Accounting Firm	Senior Accountant expert in Big Data	5 years
P31	Accountant	Accounting Firm	Senior Accountant expert in Big Data	3 years
P32	Accountant	Accounting Firm	Senior Accountant expert in Big Data	9 years
P33	Accountant	Accounting Firm	Senior Accountant expert in Big Data	12 years
P34	Accountant	Accounting Firm	Senior Accountant expert in Big Data	10 years
P35	Data Analytics auditor	Big Four	Auditor	16 years
P36	Data Analytics auditor	Big Four	Auditor	5 years
P37	Data Analytics auditor	Big Four	Auditor	5 years
P38	Data Analytics auditor	Big Four	Auditor	5 years
P39	Data Analytics auditor	Big Four	Auditor	5 years
P40	Partner– data assurance	Mid-tier audit firm	Auditor	5 years
P41	Partner– data assurance	Mid-tier audit firm	Auditor	5 years