The Impact of Big Data on Financial Reporting

This research uses quantitative methods. Seeing the development of internet technology makes it easier for us to obtain a variety of data. Big data is easier to access and offers more opportunities for analysis, therefore researchers want to conduct research on the impact of big data against the financial report. This study aims to determine the impact of big data analysis on the quality of financial statements in Indonesia. The data used in this study is primary data obtained from the study subject in the form of a questionnaire. The data obtained is then processed using Microsoft excel and SPSS 25. The results of the hypothesis test stated that the analysis of big data has an effect on the quality of the report.


INTRODUCTION
The globalization of markets and the development of new technologies are progressively changing the need for reporting, through the integration of the financial axis with concerns related to social welfare and environmental exploitation.During the Covid-19 pandemic, there has been a change or change in the way the internet is used or used.Previously, internet usage arrangements were made in the office.campuses, schools and public places.But nowadays the pattern of internet use is shifting towards homes, residences and educational institutions.In Indonesia, the number of internet users increased significantly from year to year until January 2022, with the number of internet users reaching 204.7 million people in Indonesia.The rapid development of internet technology has changed the way companies do business.Internet technology offers facilities and convenience to companies to present financial information with a high quantity, relatively cheaper cost and can reach its users without limited places and geographical barriers.The development of information and communication technology allows data sets to become more accessible.New types of data are emerging such as real-time content and Big Data Analytics (BDA) helping to solve problems between users and financial reporting and accounting.The purpose of big data in accounting is to collect, organize, and utilize data from a variety of sources to gain new business knowledge.
Big Data continues to be more accessible and offers more opportunities for analysis.As expected, the company quickly digitized and collected countless amounts of data for the purposes of forecasting, fraud prevention, and relationship building.Big data has become a widely used term to describe large data sets that require advanced data management techniques.Reporting from Kominfo.go.id,Johnny G. Plate explained, in the digital era, technological advances in the form of big data, artificial intelligence, and metaverse can enrich the data and analytics needs of the media industry for content production and distribution.The financial sector generates large amounts of data such as customer data, records of their financial products, transaction data that can be used to support decision making, as well as external data, such as social media data and website data.The Deputy Director of Academic Affairs points out that the ability to navigate transitions and adapt to digital advancements is the key to success as an accountant in the digital age.Therefore, the accountant must certainly deepen his knowledge and understanding of technology and big data will create added value and opportunities, not only for individuals but also for entities."If we or accountants don't follow digital transformation, it won't be an opportunity but it will turn into a challenge, even a threat." [1] According to the U.S. Bureau of Labor Statistics, employment for accountants and auditors grew by 4% between 2019 and 2029.U.S. News & World Report mentioned that accountants ranked thirteenth on the list of best business jobs for 2021.According to the December 2016 issue of the International Edition of Accounting and Business Magazine, Roger Leonard Burrit and Kathrine Christ mentioned four steps an accountant must take to deal with the Industrial Revolution 4.0.One of them applies high standards (Reaching Out).In this application, accountants are required to have maximum control over the data done so that accountants must coordinate with engineers so that data and information can be maintained properly.Big data is not limited to accounting and financial data.Big data includes financial and non-financial data, accounting and non-accounting data, as well as quantitative and qualitative data, available en masse, in various formats, and over time.If high-quality data is available and processed in real time, it [2] means that the company will be able to provide more accurate financial statements [3].
For companies with increasing data volume, companies have two options, using the data to increase competitiveness or ignore the data.Businesses always use data and information to support decision-making and operations management.Big data includes many types of data that can be utilized such as images, voice recordings and video.To convert data into value added companies must use data analysis tools.Companies need a need for data to develop accounting criteria with a focus on analytical data to improve information, investor attractiveness and capital market efficiency.Big data is one of the most pressing challenges in the accounting profession, which faces many challenges such as rapid financial and economic changes, financial corruption, globalization, and unfamiliarity with new developments in information technology.Recently, the topic of the quality of accounting information has become very important because the crisis of business organizations has a negative impact on users of financial statements.According to Gordon B. Davis, an expert in information systems management, data is symbols or logic that must be arranged and arranged so that we can get results from the data itself.Technology brings convenience to business.This led to the emergence of many dynamic new businesses and increased competition.Previous researchers conducted research on the impact of Big Data Analytics on improving financial statements in the Saudi environment.Based on this background, the author is interested in analyzing the impact of Big Data on financial statements in Indonesia.The purpose of this study is to find out what big data is and its impact on financial statements in Indonesia.

THEORETICAL FOUNDATION 2.1 Theoretical Studies
2.1.1Big Data.Big data is a large, diverse, and rapid flow of information that requires the use of innovative and economically efficient processing methods for the purpose of developing decision support methods and automating processes.[4]Big data is a data set characterized by quantity, speed, variability, variance, accuracy, and validity.It cannot be processed efficiently by traditional technology.[4] Big data is divided into 3; First, structured data is data stored in a database field.It can be managed, analyzed and searched using SQL (Structured Query Language).Both unstructured data is data that cannot be easily classified such as images, graphics, video clips, PDFs, presentations, emails, tweets, web pages, Facebook messages, conversations message.Third.Semi-structured data is a mixture of structured and unstructured data, but it lacks an organized structure like a word processing program [4].There are five characteristics of big data namely: 1. Volume: big data is characterized by a greater amount of data than data from traditional methods, 2. Speed: Big data is generated faster than data from traditional methods, so it has rushed speeds due to active interaction with personal audiences, customers, and beneficiaries.3. Diversity: big data is more diverse than traditional accounting data because it contains other data such as images, video, audio, and text.4. Authenticity: refers to the reliability of the data because the receiver cares about the quality of the data.5. Value: indicates that data contributes to making timely decisions [4] .
2.1.2Big Data on Financial Report.Quality is a set of characteristics that accounting information must possess in order to be useful in meeting user needs, such as understanding, reliability, relevance, relevance, comparability, and, above all, association with information competence .[5]Make a difference in the user decisions of the report.The quality of accounting information is defined as the characteristics of accounting information contained in financial statements.These characteristics help to assess the level of quality of accounting information, decision making, and forecasting financial failure.The quality of accounting information is characterized by understanding, relevance, reliability, and comparability [4].Financial reports are the final product of financial accounting and are of primary interest to management and stakeholders.However, company reports do not meet changing user needs over time.First, financial statements are still presented on a quarterly, semesterly, and yearly basis in the era of big data.Typically, a report is made publicly available after being audited at the end of the financial year, which implies that it may lose its relevance.Investors and other stakeholders need timely financial information, perhaps on a daily basis.In this case, one of the dimensions of big data is speed, which refers to the speed of processing and creating data from; Big data systems are capable of processing and generating real-time data that can help companies publish their financial statements in a short period of time.This suggests that when companies adopt big data systems, it can significantly affect their ability to make financial information available to the public within the desired time frame.[3] In general, big data and its relationship to the quality of financial statements can be seen through the four dimensions of "Vs" (volume, speed, variety, and authenticity).Because some data sources provide greater confidence in this data, that is reflected in the quality of the information in the financial statements and allows companies to improve the information.As they give, here the emphasis emphasizes on the role of business intelligence techniques in processing this data.Production in an organized and knowledgeable manner [1].In terms of speed, what is meant by the rate of growth and production of data or the rate of data extraction, which is the time we take from the moment the data arrives until we make a decision based on the data.[6]Speed is a key factor in making these data-driven decisions, due to the "novelty" of the data that decision makers can collect, as well as the ability to analyze data paths.This is a key factor in increasing the speed of the company and enabling real-time action and decision making during the day.The role of business intelligence technology has emerged with a significant increase in the level of data frequency, and this is also due to the diversity of its sources.The need is becoming increasingly urgent for systems that guarantee high speed in real-time big data analysis or instantaneous convergence rates over time.
2.1.3Big Data on the Role of Accountant.Big data significantly affects the role of accountants, as it creates opportunities for them to move into strategic roles in business organizations and helps accountants transform from decision makers into business partners.[4] Finance and accounting professionals are increasingly implementing Big Data in their businesses, and many financial leaders expect significant changes for 2020 in business and their accountants need to develop the skills necessary to keep pace with the technology and behavior of strategic business partners in organizations.Accounting programs should include experiential learning that develops information technology skills integrated with accounting.This educational experience includes skills in data creation, data management and analysis, data communication, data security, and storage.In addition, data analysis, information technology skills, and knowledge development should be important components of accounting programs.[4] [7] Big data includes: 1) Image and video data: Videos containing employee productivity tracking videos, inventory videos to measure productivity and identify bottlenecks, interviews in which management interviews are analyzed to extract content, emotions, and then provide non-verbal information about risks and the big picture of the business.This nonverbal component is more important than the verbal content in which it creates a picture of management's intentions.2) Audio data: Audio data analysis can provide quarterly benefits, such as quarterly conference calls, shareholder and board meetings, customer calls, employees' internal phone calls.3) Text data: including websites, Facebook, and Twitter.This data is useful for marketing support, providing early warning of product failures, estimating sales volume, and evaluating and improving business performance.Companies extract and combine big data formats from video, images, audio, and text with traditional financial data to promote accounting records, improve the quality of financial information, and promote transparency, streamline decision-making, and respond to stakeholder requests.[4,18].Big data influenced the future of financial reporting and the evolution of GAAP, where Extensible Business Reporting Language (XBRL), originally XML (Extensible Markup Language), could be used.This language can process data automatically and is able to convert data into valuable information.A Base-Based Report (XBRL) is an electronic version of an enhanced financial statement that reduces data processing time, prepares environmentally friendly electronic financial statements, and improves analysis Analyzing financial information, publishing reports in multiple languages, and improving financial disclosures with the aim of helping investors obtain accurate information.Big data analytics will provide real-time reporting in large companies, even small businesses trying to capitalize on the benefits of big data in accounting, such as Xero Company, which provides cloud computing accounting software for small businesses to help them take advantage of big data opportunities in accounting.[8]

Previous Research
The first study titled "The Impact of Big Data Analytics on Improving Financial Reporting Quality" was created by Nagat Mohamed Marie Younis in 2020.In such research, business organizations benefit from big data analytics.The most important benefits among them are: helping to provide a comprehensive view of the economic unit, building the strategy and business model of the economic unit, creating a high competitive advantage for formation, improving the quality of accounting information, providing relevant information that helps to rationalize decisions in economic units, determining past performance and future sources of information.On the other hand, big data analytics faces a number of challenges, the most important of which are: lack of a workforce specializing in big data analysis, high cost of hiring experienced experts, speed of Bigdata data flow affects the rationalization of decision making, difficulty in transferring, storing, and processing big data, difficulty in presenting analytics related to big data.The study concluded that big data analysis improves the quality of accounting information, as it clearly affects the qualitative characteristics of accounting information, which positively affects the quality of financial statements.[4,16] The second study titled "The Moderating Role of Business Intelligence iin the Impact of Big Data on Financial Reports Quality in Jordanian Telecom Companies" was created by Omar Muhammad Zraqat In 2020.In this research business intelligence technology used in new technologies of the digital economy helps improve security and efficiency in the use of information, an important resource in the digital economy, where data related to business economic and financial activities can be recorded and stored more reliably, which accelerates the processing and verification of records when using business intelligence technology, where business intelligence contributes to processing data in quantity.large and by monitoring the flow of accounting figures in real time.Big data encourages the preparation of accounting reports in a stream of specially formatted data available in the near future through the use of modern information technology, enabling all parties to work with this type of modern information technology.Big data technology automatically verifies important information in the form of financial (accounting) and non-financial statements.This will significantly reduce the cost and time it takes to prepare financial statements.Large volumes of data do not affect the quality of financial statements.This is likely due to the increasing amount of data collected due to the speed and variety of available resources, companies do not need to have it all for as long as they can access, and therefore, large data flows can hinder the authors of financial statements, and thus have no effect on improving the quality of financial statements.It is difficult for companies to manage large amounts of data.[1,17] The third study titled "Big Data Analytics in Financial Reporting and Accounting" was created by Kaya, I., Akbulut, D. H.In 2018 [9].In the study, the results of interviews with academics and accounting professionals produced the following findings: 1.There have been so many changes in information and communication technology, and accountants need to keep up with the changes, complexities, costs, and risks associated with their implementation.For example, revenue recognition in many industries, including aviation and telecommunications, requires processing large amounts of data due to complex payment systems.In addition to managing the overall availability of information, its usefulness in decision-making, integrity, quality, and confidentiality, the presentation of this information in the annual Audit financial statements is a major challenge for accountants.Furthermore, big data and analytics present a pressing problem and at the same time are real opportunities for accountants working in forensic science and assessment.
2. There are many opportunities to connect traditionally expanded data, such as data found in ERP, with new data sources.The expanded ERP system adds to the usefulness of accounting records with BDA regarding the condition, character, and characteristics of the asset.Processing of nontraditional big data from various sources has a positive effect on the accuracy of financial reporting.
3. BDA solution providers play an important role in the implementation of BDA; Usually, they understand the needs of the business and offer solutions.
4. Institutional pressure sometimes plays an important role in IT investment decisions.Some companies follow BDA policies and procedures adopted by other companies to try to ensure the legitimacy of their business and maintain and increase their competitive advantage.
5. Academics and accounting professionals are trying to adapt to the challenges of BDA in using and mastering new technologies and applications.They realize that they need to improve their competence and skills in the field of big data analytics.
The fourth study entitled "The Convergence of Big Data and Accounting: Innovative Research Opportunities" was conducted by Ole Awad Elsayed Awad Ibrahim, PhD, Ahmed A, Elamer, PhD, Amr Nazieh Ezat, PhD in 2021 [3].In this study the authors argue that big data can overcome the limitations of some data-dependent accounting techniques, such as financial reporting, performance measurement, and audit evidence.The study also argues that Big Data as a new technology will reshape accounting because data is at the heart of accounting.In addition, the author's discussion of the convergence between accounting and big data allows authors to suggest that big data users are more likely to provide measurable and high-quality financial statements, and manage more effectively, provide more precise and complete audit evidence, and manage more.Risking.efficiency, fewer budget gaps, and conducting business analysis more efficiently than those who don't use big data.
The fifth study entitled "Big Data and Corporate Reporting: Impacts and Paradoxes" was made by Al-Htaybat, K., & Von Alberti-Alhtaybat, L. (2017).In this study the author explained that accountants need to engage with different parts of the organization and must be proactive when it comes to big data and business reporting.Since they must provide large amounts of data to stakeholders and collect and analyze big data, accountants must engage with data scientists to achieve the most meaningful results together.In addition, providing more organizational data and information can reduce information asymmetry, which can have a positive impact on investor confidence in accounting practices and the business as a whole.Advice for research to be done in the future, as this is still a new area of research to be done.Substantial additional empirical analysis is required on big data analysis that is currently or likely to be used in accounting in general and corporate reporting in particular, through case studies or detailed information, through quantitative surveys to gather broader information.Future research should focus more on how to use digital content analytics, such as data analytics, multimedia analytics, text analytics, web analytics, network analytics, and Mobile analytics analytics in business reporting.In addition, the educational curriculum needs to be addressed in future research, since accounting programs have not been fully involved in technological developments and accounting information systems courses are still relevant theory with little practical knowledge of the relationship between technology and real-world accounting.Finally, future research should consider accounting standards and theoretical frameworks to determine whether standards need to be changed and how to enable more flexible provision and guidance of information for the future of big data analytics. [10]

METHODOLOGY 3.1 Data Types and Sources
This research is quantitative research.According to Emzir (2009: 28) Quantitative methods are approaches that primarily use postdisaster theory in the development of knowledge (such as those related to cause and effect, reduction of variables, hypotheses and specific questions with measurement, observation, and testing theory), using research strategies such as surveying and statistical investigation experiments.The study used primary data.According to Husein Umar (2013) primary data can be interpreted as data obtained from primary sources, or from individuals / individuals as a result of interviews, or obtained from the results of filling out questionnaires conducted by researchers.The primary data source is the respondent or direct subject of the research.So that researchers can immerse themselves in observing and writing down answers directly from the research subject.Key data is data that can be obtained in a variety of ways, such as questionnaires, face-to-face interviews, or surveys, so its existence is necessary to help solve the problem.

Research Models and Hypotheses
This research is considered one of the applied research in solving field problems and developing work methods and productivity in the field of accounting.The study aims to identify the opinions and attitudes of big data analysis by 2022 through a shared quaternary to describe the impact of big data on financial reports.

Research Objects
Employees working in accounting in 2022 in Indonesia.

Reliability Test
The stability of the tool used to measure variables in the study was tested using the cronbach alpha coefficient test, where the scale result is statistically accepted if the cronbach alpha value is greater than (0.60), and close to the value (100%), indicating a higher level of stability of the research instrument.[1]

One Sample T Test
A single sample test is used to compare the average of a sample with a specific value.Researchers can use the sample to compare the sample mean with the hypothetical population mean to see if the sample differs significantly.For example, sample tests are used to compare sample averages and sample midpoints of test variables and also to determine whether observed patterns can be generated by valid processes.Certain average or not.A sample test assumes that the dependent variables are normally distributed in the population and that the data is independent.The procedures used to perform sample tests are the same as those used when conducting independent sample tests and dependent sample tests.Test samples can be calculated using formulas or statistical packages for social sciences (SPSS [9] ).

RESULTS AND DISCUSSIONS 4.1 Distribution of Research Samples
Based on the data contained in Table 1, the number of respondents who have less than 5 years of work experience amounts to 16

Reliability Test
Based on Table 2 the reliability test gets a Cronbach alpha value of 0.425 which means that the data obtained has a fairly reliable data reliability level because it is more than 0.40.

Normal Distribution Testing
In table 3 it is known that the value of Asymp Sig.(2-Tailed) of 0.200 which is greater than 0.05, therefore it can be concluded that the figure shows the data following the normal distribution.

Relative Importance of Variables
Table 4 presents the relative importance of respondents' opinions about the impact of big data analysis on the accounting information cauldron.Relative interests are calculated using formulas:

𝐴 * 𝑁
Source: [4] From Table 4 in the opinion of accountants the most important element according to relative interest standards are: •Big data analysis leads to an increased understanding and analytics of information accounting content.
•Big data analysis leads to increased opportunities for future growth, and increased predicts the future performance of economic units.
•Big data analysis leads to an increase in the performance assessment of the company, which increases the level of trust of accounting information.
•Big data analysis leads to know the latest developments of the economic unit, which improves the quality of accounting information.

One Sample T Test
The standard deviation reflects the average deviation of the data from the average.Standard deviation can describe a change in data, where if the standard deviation is greater than the mean, it  6 shows the SPSS Output Analysis obtained a Sig value.(2tailed) of 0.000 which is smaller than 0.05, it can be concluded that H0 is rejected which means that big data analysis has an effect on improving the quality of financial statements.Table 6 also describes a positive t value with a value of 23.974, a confidence interval of a lower difference of 10.35, and a higher value of 12.37.

CONCLUSIONS AND SUGGESTIONS
This research aims to find out about the impact of big data on the quality of baking reports in Indonesia in the opinion of accountants.The purpose of financial statements is to provide information about the company's financial position, operations and changes in financial position that are useful for those with an interest in making economic decisions.In the opinion of the respondents big data analysis allows for increased understanding and analysis of information content, increases future growth opportunities, and improves the forecast of future performance of economic units.Based on the data obtained from respondents, it can be concluded that the use of big data can provide high-quality financial statements.
Researchers suggest that including big data subjects in accounting education at universities.Big Data is one of the pressing challenges facing the accounting profession with many challenges such as rapid economic and financial changes, financial corruption, globalization and ignorance with new developments in the IT field.Big data significantly affects the role of accountants, as it gives them the opportunity to move into strategic roles in business organizations and helps accountants move from decision makers to business partners (Younis, 2020).By studying big data, accountants can increase their value and role in the future.

Table 1 :
Research Sample By Year of Experience and Breadth of Respondents' Need to Analyze Big Data [1]S 25 is used to analyze research data.Statistical tools used include descriptive statistical measures, such as arithmetic mean, standard deviation, repetition, and percentage.Internal consistency coefficient (Cronbach Alpha Coefficient) to check the stability of research tools.Pearson correlation coefficient for checking for multicollinearity.Multiple and progressive linear regression, to test the influence of free variables on bound variables.And tiered regression analysis, to test the influence of free variables on variables bound by the presence of censored variables .[1]

Table 2 :
Reliability Test

Table 5 :
Statistics one sample

Table 6 :
One Sample Test to Compare Between Collective Questions and Common Mean of answers mean is a poor representation of the whole data.In table5the mean value reaches the number 43.31 with a standard deviation of 2,669.Since the mean value is greater than the standard deviation, this means that the acceptance rate of the study sample for all proposed variables is very high.Table