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Accounting and Data Analytics: A Transformative Partnership
Author: Dr. Anya Sharma, CPA, PhD in Data Science. Dr. Sharma is a Professor of Accounting and Information Systems at the University of California, Berkeley, with over 15 years of experience in both academic research and practical application of accounting and data analytics in the corporate world. Her expertise lies in developing and implementing advanced analytics techniques to enhance accounting processes and improve decision-making.
Publisher: Published by the Institute of Management Accountants (IMA), a globally recognized professional association for accountants and financial professionals. The IMA is widely respected for its rigorous standards and commitment to advancing the accounting profession, making it a credible source for information on accounting and data analytics.
Editor: Edited by Mr. David Chen, CA, a seasoned accounting professional with 20 years of experience in financial reporting and data analysis within Fortune 500 companies. Mr. Chen has extensive experience in leveraging data analytics to improve financial forecasting and risk management, making him ideally suited to oversee this publication.
Summary: This report explores the burgeoning field of accounting and data analytics, detailing its transformative impact on the accounting profession. It analyzes the various applications of data analytics within accounting, from automating routine tasks to uncovering fraud and providing valuable insights for strategic decision-making. Research findings highlight the increased efficiency, accuracy, and improved forecasting capabilities enabled by integrating data analytics into accounting practices. The report concludes that accounting and data analytics are inextricably linked, and professionals must adapt to this evolving landscape to remain competitive.
1. The Evolution of Accounting: Embracing Data Analytics
The traditional role of an accountant has significantly evolved. No longer solely focused on manual data entry and ledger maintenance, accountants are increasingly leveraging accounting and data analytics to extract meaningful insights from vast datasets. This shift is driven by several factors, including the exponential growth of data generated by businesses, the increasing availability of powerful analytical tools, and the growing demand for data-driven decision-making. Research by the American Accounting Association (AAA) shows a significant increase in job postings requiring data analytics skills for accounting professionals.
2. Key Applications of Data Analytics in Accounting
Accounting and data analytics are intertwined in numerous ways, creating efficiencies and providing strategic insights:
Financial Forecasting and Budgeting: Advanced analytical techniques, such as machine learning, can analyze historical financial data to build more accurate predictive models for revenue, expenses, and cash flow. This allows businesses to make better-informed budgeting and forecasting decisions. Studies have shown that businesses employing data-driven forecasting experience significantly lower forecast errors.
Fraud Detection: Accounting and data analytics are powerful tools for identifying fraudulent activities. Anomaly detection algorithms can flag suspicious transactions and patterns that might otherwise go unnoticed. Research indicates that data analytics has significantly increased the efficiency and effectiveness of fraud detection in organizations.
Audit Optimization: Data analytics can streamline the audit process by automating data collection, analysis, and reporting. This reduces audit time and costs while improving the quality and reliability of audit findings. A study by Deloitte found that the use of data analytics in auditing reduced audit time by an average of 20%.
Risk Management: By analyzing various internal and external data sources, accounting professionals can better assess and mitigate financial risks. This includes identifying potential credit risks, market risks, and operational risks.
Performance Analysis: Data analytics helps companies gain deeper insights into their financial performance, allowing them to identify areas for improvement and optimize resource allocation. KPI dashboards powered by accounting and data analytics provide real-time monitoring and allow for faster, data-driven responses.
3. Data Sources for Accounting and Data Analytics
The effectiveness of accounting and data analytics hinges on access to reliable and relevant data sources. These include:
Internal Data: Financial transactions, customer data, sales data, operational data, and inventory data.
External Data: Market data, economic indicators, competitor information, and regulatory data.
Third-Party Data: Data from credit bureaus, supply chain partners, and other external sources.
The effective integration and analysis of these diverse data sources is crucial for obtaining comprehensive insights.
4. Skills Required for Accounting Professionals in the Age of Data Analytics
The increasing importance of accounting and data analytics necessitates a shift in the skillset of accounting professionals. Beyond traditional accounting knowledge, professionals need to develop proficiency in:
Data Wrangling and Preprocessing: Cleaning, transforming, and preparing data for analysis.
Statistical Analysis: Applying statistical methods to analyze data and draw meaningful conclusions.
Data Visualization: Creating effective data visualizations to communicate findings effectively.
Programming Languages (e.g., Python, R): Utilizing programming languages to perform data analysis and build predictive models.
Database Management: Managing and querying large datasets.
Machine Learning and Artificial Intelligence: Applying advanced techniques to extract insights from complex data.
5. Challenges and Opportunities in Implementing Accounting and Data Analytics
Despite the numerous benefits, implementing accounting and data analytics presents challenges:
Data Security and Privacy: Protecting sensitive financial data is paramount. Robust security measures are crucial.
Data Quality: Inaccurate or incomplete data can lead to flawed conclusions. Data quality management is essential.
Integration with Existing Systems: Integrating data analytics tools with existing accounting systems can be complex and time-consuming.
Cost of Implementation: Investing in the necessary software, hardware, and training can be expensive.
However, these challenges are outweighed by the significant opportunities offered by accounting and data analytics, driving innovation and efficiency within the accounting profession.
6. The Future of Accounting and Data Analytics
The integration of accounting and data analytics is not a trend; it's a fundamental transformation. Future developments will likely include:
Increased Automation: Further automation of accounting processes using Robotic Process Automation (RPA) and AI.
Advanced Predictive Analytics: More sophisticated predictive models for improved forecasting and risk management.
Real-time Insights: Access to real-time data and insights for faster decision-making.
Blockchain Technology: Utilizing blockchain for enhanced security and transparency in financial transactions.
Conclusion:
The convergence of accounting and data analytics represents a significant paradigm shift in the accounting profession. By embracing data analytics, accountants can enhance efficiency, improve decision-making, mitigate risks, and unlock new opportunities for growth and innovation. The challenges associated with implementation are surmountable, and the rewards far outweigh the costs. The future of accounting lies in leveraging the power of data to provide greater value to businesses and stakeholders.
FAQs:
1. What is the difference between traditional accounting and accounting with data analytics? Traditional accounting primarily focuses on manual data processing and reporting, while accounting with data analytics utilizes advanced techniques to extract insights from large datasets.
2. What programming languages are most useful for accounting and data analytics? Python and R are widely used due to their extensive libraries for data analysis and machine learning.
3. How can data analytics help prevent fraud? Anomaly detection algorithms can identify unusual transactions and patterns that might indicate fraudulent activity.
4. What are the ethical considerations of using data analytics in accounting? Maintaining data privacy and security, ensuring data accuracy, and avoiding bias in analysis are key ethical considerations.
5. What are the career prospects for accountants with data analytics skills? Accountants with data analytics skills are in high demand, with opportunities in various industries and roles.
6. What type of software is used in accounting and data analytics? Popular software includes tools like Tableau, Power BI, Alteryx, and specialized accounting software with embedded analytics.
7. How can businesses implement accounting and data analytics? A phased approach, starting with a pilot project and gradually scaling up, is often recommended.
8. What is the return on investment (ROI) of accounting and data analytics? The ROI can vary depending on implementation and usage, but improved efficiency, reduced errors, and better decision-making contribute significantly.
9. How can I learn more about accounting and data analytics? Online courses, certifications, and advanced degrees are excellent resources for enhancing skills in this field.
Related Articles:
1. "The Impact of Big Data on Financial Reporting": Explores how big data analytics transforms financial reporting, enhancing accuracy and timeliness.
2. "Data Analytics for Fraud Detection in the Financial Services Industry": Focuses on the application of data analytics in identifying fraudulent activities within financial institutions.
3. "Implementing Data Analytics in Internal Audit: A Practical Guide": Provides a step-by-step guide for integrating data analytics into internal audit processes.
4. "Predictive Analytics in Financial Forecasting: Improving Accuracy and Decision-Making": Discusses the use of predictive analytics to enhance financial forecasting and budgeting.
5. "Data Visualization Techniques for Communicating Financial Insights": Explores various data visualization techniques for effectively communicating financial information.
6. "The Role of Machine Learning in Accounting and Finance": Examines the application of machine learning algorithms in solving accounting and finance problems.
7. "Cybersecurity and Data Protection in Accounting and Data Analytics": Addresses the crucial aspects of protecting sensitive financial data in the age of data analytics.
8. "Cloud Computing and its Impact on Accounting and Data Analytics": Discusses the advantages and challenges of using cloud computing for accounting and data analytics.
9. "The Future of Work for Accountants in the Age of Automation": Explores how automation powered by data analytics is transforming the accounting profession and the skills needed to succeed.
| accounting and data analytics: Data Analytics for Accounting Vernon J. Richardson, Ryan Teeter, Katie L. Terrell, 2018-05-23 |
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| accounting and data analytics: Data Mining For Dummies Meta S. Brown, 2014-09-29 Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining. |
| accounting and data analytics: Financial Data Analytics Sinem Derindere Köseoğlu, 2022-04-25 This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. |
| accounting and data analytics: Quantitative Data Analysis Willem Mertens, Amedeo Pugliese, Jan Recker, 2016-09-29 This book offers postgraduate and early career researchers in accounting and information systems a guide to choosing, executing and reporting appropriate data analysis methods to answer their research questions. It provides readers with a basic understanding of the steps that each method involves, and of the facets of the analysis that require special attention. Rather than presenting an exhaustive overview of the methods or explaining them in detail, the book serves as a starting point for developing data analysis skills: it provides hands-on guidelines for conducting the most common analyses and reporting results, and includes pointers to more extensive resources. Comprehensive yet succinct, the book is brief and written in a language that everyone can understand - from students to those employed by organizations wanting to study the context in which they work. It also serves as a refresher for researchers who have learned data analysis techniques previously but who need a reminder for the specific study they are involved in. |
| accounting and data analytics: Cost Accounting Karen Congo Farmer, Amy Fredin, 2022-02-08 Cost Accounting with Integrated Data Analytics takes the approach that you need to reach students in order to engage and effectively teach them to make meaning of costing concepts. Through storytelling, students develop a deeper understanding of cost accounting fundamentals, allowing them to apply their knowledge to modern business scenarios and develop the competencies and decision-making skills needed to become the future accounting professional. Throughout Cost Accounting, students also work through a variety of data analysis applications that allow them to develop their decision-making skills within real-world contexts. Through assignments and integrated cases that leverage market-leading technology, students learn how to make informed business decisions and think critically about data. |
| accounting and data analytics: Data and Analytics in Accounting Ann C. Dzuranin, Guido Geerts, Margarita Lenk, 2022-12-20 Develop an integrated data analysis and critical thinking skill set needed to be successful in the rapidly changing accounting profession. Data Analytics in Accounting: An Integrated Approach, 1st Edition helps students develop the professional skills you need to plan, perform, and communicate data analyses effectively and efficiently in the real world. An integrated approach provides flexibility for use within a standalone course or across the accounting curriculum. |
| accounting and data analytics: Accounting Information Systems Arline A. Savage, Danielle Brannock, Alicja Foksinska, 2024-01-08 |
| accounting and data analytics: Analytics and Big Data for Accountants Jim Lindell, 2020-12-03 Why is big data analytics one of the hottest business topics today? This book will help accountants and financial managers better understand big data and analytics, including its history and current trends. It dives into the platforms and operating tools that will help you measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators. Key topics covered include: Evidence-based techniques for finding or generating data, selecting key performance indicators, isolating program effects Relating data to return on investment, financial values, and executive decision making Data sources including surveys, interviews, customer satisfaction, engagement, and operational data Visualizing and presenting complex results |
| accounting and data analytics: Fourth Industrial Revolution and Business Dynamics Nasser Rashad Al Mawali, Anis Moosa Al Lawati, Ananda S, 2021-10-07 The book explains strategic issues, trends, challenges, and future scenario of global economy in the light of Fourth Industrial Revolution. It consists of insightful scientific essays authored by scholars and practitioners from business, technology, and economics area. The book contributes to business education by means of research, critical and theoretical reviews of issues in Fourth Industrial Revolution. |
| accounting and data analytics: Data Analytics for Accounting Vernon J. Richardson, Ryan Teeter, Katie Terrell, 2019 |
| accounting and data analytics: Handbook of Big Data and Analytics in Accounting and Auditing Tarek Rana, Jan Svanberg, Peter Öhman, Alan Lowe, 2023-02-03 This handbook collects the most up-to-date scholarship, knowledge, and new developments of big data and data analytics by bringing together many strands of contextual and disciplinary research. In recent times, while there has been considerable research in exploring the role of big data, data analytics, and textual analytics in accounting, and auditing, we still lack evidence on what kinds of best practices academics, practitioners, and organizations can implement and use. To achieve this aim, the handbook focuses on both conventional and contemporary issues facing by academics, practitioners, and organizations particularly when technology and business environments are changing faster than ever. All the chapters in this handbook provide both retrospective and contemporary views and commentaries by leading and knowledgeable scholars in the field, who offer unique insights on the changing role of accounting and auditing in today’s data and analytics driven environment. Aimed at academics, practitioners, students, and consultants in the areas of accounting, auditing, and other business disciplines, the handbook provides high-level insight into the design, implementation, and working of big data and data analytics practices for all types of organizations worldwide. The leading scholars in the field provide critical evaluations and guidance on big data and data analytics by illustrating issues related to various sectors such as public, private, not-for-profit, and social enterprises. The handbook’s content will be highly desirable and accessible to accounting and non-accounting audiences across the globe. |
| accounting and data analytics: Guide to Audit Data Analytics AICPA, 2018-02-21 Designed to facilitate the use of audit data analytics (ADAs) in the financial statement audit, this title was developed by leading experts across the profession and academia. The guide defines audit data analytics as “the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for planning or performing the audit.” Simply put, ADAs can be used to perform a variety of procedures to gather audit evidence. Each chapter focuses on an audit area and includes step-by-step guidance illustrating how ADAs can be used throughout the financial statement audit. Suggested considerations for assessing the reliability of data are also included in a separate appendix. |
| accounting and data analytics: Customer Accounting Massimiliano Bonacchi, Paolo Perego, 2018-11-04 This book is designed to meet the needs of CFOs, accounting and financial professionals interested in leveraging the power of data-driven customer insights in management accounting and financial reporting systems. While academic research in Marketing has developed increasingly sophisticated analytical tools, the role of customer analytics as a source of value creation from an Accounting and Finance perspective has received limited attention. The authors aim to fill this gap by blending interdisciplinary academic rigor with practical insights from real-world applications. Readers will find thorough coverage of advanced customer accounting concepts and techniques, including the calculation of customer lifetime value and customer equity for internal decision-making and for external financial reporting and valuation. Beyond a professional audience, the book will serve as ideal companion reading for students enrolled in undergraduate, graduate, or MBA courses. |
| accounting and data analytics: Auditing Raymond N. Johnson, Laura Davis Wiley, Robyn Moroney, Fiona Campbell, Jane Hamilton, 2019-05-20 The explosion of data analytics in the auditing profession demands a different kind of auditor. Auditing: A Practical Approach with Data Analytics prepares students for the rapidly changing demands of the auditing profession by meeting the data-driven requirements of today's workforce. Because no two audits are alike, this course uses a practical, case-based approach to help students develop professional judgement, think critically about the auditing process, and develop the decision-making skills necessary to perform a real-world audit. To further prepare students for the profession, this course integrates seamless exam review for successful completion of the CPA Exam. |
| accounting and data analytics: Forensic Analytics Mark J. Nigrini, 2020-05-12 Become the forensic analytics expert in your organization using effective and efficient data analysis tests to find anomalies, biases, and potential fraud—the updated new edition Forensic Analytics reviews the methods and techniques that forensic accountants can use to detect intentional and unintentional errors, fraud, and biases. This updated second edition shows accountants and auditors how analyzing their corporate or public sector data can highlight transactions, balances, or subsets of transactions or balances in need of attention. These tests are made up of a set of initial high-level overview tests followed by a series of more focused tests. These focused tests use a variety of quantitative methods including Benford’s Law, outlier detection, the detection of duplicates, a comparison to benchmarks, time-series methods, risk-scoring, and sometimes simply statistical logic. The tests in the new edition include the newly developed vector variation score that quantifies the change in an array of data from one period to the next. The goals of the tests are to either produce a small sample of suspicious transactions, a small set of transaction groups, or a risk score related to individual transactions or a group of items. The new edition includes over two hundred figures. Each chapter, where applicable, includes one or more cases showing how the tests under discussion could have detected the fraud or anomalies. The new edition also includes two chapters each describing multi-million-dollar fraud schemes and the insights that can be learned from those examples. These interesting real-world examples help to make the text accessible and understandable for accounting professionals and accounting students without rigorous backgrounds in mathematics and statistics. Emphasizing practical applications, the new edition shows how to use either Excel or Access to run these analytics tests. The book also has some coverage on using Minitab, IDEA, R, and Tableau to run forensic-focused tests. The use of SAS and Power BI rounds out the software coverage. The software screenshots use the latest versions of the software available at the time of writing. This authoritative book: Describes the use of statistically-based techniques including Benford’s Law, descriptive statistics, and the vector variation score to detect errors and anomalies Shows how to run most of the tests in Access and Excel, and other data analysis software packages for a small sample of the tests Applies the tests under review in each chapter to the same purchasing card data from a government entity Includes interesting cases studies throughout that are linked to the tests being reviewed. Includes two comprehensive case studies where data analytics could have detected the frauds before they reached multi-million-dollar levels Includes a continually-updated companion website with the data sets used in the chapters, the queries used in the chapters, extra coverage of some topics or cases, end of chapter questions, and end of chapter cases. Written by a prominent educator and researcher in forensic accounting and auditing, the new edition of Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations is an essential resource for forensic accountants, auditors, comptrollers, fraud investigators, and graduate students. |
| accounting and data analytics: Cost Accounting Margaret H. Christ, D. Kip Holderness (Jr.), Vernon J. Richardson, 2024 The role of management accountants is to analyze data to help organizations make effective business decisions. Thanks to an ever-increasing amount of data generated by companies, the opportunities for management accountants to provide data-driven insights have never been greater. We believe that students can prepare for an accounting career not only by understanding the methods and procedures of cost accounting but also by learning how to examine and analyze data, interpret the results, and share insight with others in their organizations-- |
| accounting and data analytics: Forensic Analytics Mark J. Nigrini, 2011-05-12 Discover how to detect fraud, biases, or errors in your data using Access or Excel With over 300 images, Forensic Analytics reviews and shows how twenty substantive and rigorous tests can be used to detect fraud, errors, estimates, or biases in your data. For each test, the original data is shown with the steps needed to get to the final result. The tests range from high-level data overviews to assess the reasonableness of data, to highly focused tests that give small samples of highly suspicious transactions. These tests are relevant to your organization, whether small or large, for profit, nonprofit, or government-related. Demonstrates how to use Access, Excel, and PowerPoint in a forensic setting Explores use of statistical techniques such as Benford's Law, descriptive statistics, correlation, and time-series analysis to detect fraud and errors Discusses the detection of financial statement fraud using various statistical approaches Explains how to score locations, agents, customers, or employees for fraud risk Shows you how to become the data analytics expert in your organization Forensic Analytics shows how you can use Microsoft Access and Excel as your primary data interrogation tools to find exceptional, irregular, and anomalous records. |
| accounting and data analytics: Audit Analytics in the Financial Industry Jun Dai, Miklos A. Vasarhelyi, Ann Medinets, 2019-10-28 Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes. |
| accounting and data analytics: Analytics and Big Data for Accountants Jim Lindell, 2018-04-11 Analytics is the new force driving business. Tools have been created to measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators, many using the unprecedented amount of data now flowing into organizations. Featuring updated examples and surveys, this dynamic book covers leading-edge topics in analytics and finance. It is packed with useful tips and practical guidance you can apply immediately. This book prepares accountants to: Deal with major trends in predictive analytics, optimization, correlation of metrics, and big data. Interpret and manage new trends in analytics techniques affecting your organization. Use new tools for data analytics. Critically interpret analytics reports and advise decision makers. |
| accounting and data analytics: Continuous Auditing David Y. Chan, Victoria Chiu, Miklos A. Vasarhelyi, 2018-03-21 Continuous Auditing provides academics and practitioners with a compilation of select continuous auditing design science research, and it provides readers with an understanding of the underlying theoretical concepts of a continuous audit, ideas on how continuous audit can be applied in practice, and what has and has not worked in research. |
| accounting and data analytics: Loose Leaf for Data Analytics for Accounting Vernon Richardson, Professor, 2018-04-30 Data Analytics in Accounting is designed to prepare your students with the necessary tools and skills they need to successfully perform data analytics. Using Isson's data analytics model, the IMPACT Cycle, the authors provide a conceptual framework to help students think through the steps needed to provide data-driven insights and recommendations. Integrated in each chapter, labs provide multiple datasets and tutorials to give students hands-on experience working with different types of data and the tools used to analyze it. Students will conduct data analysis using Excel, Access (including SQL), Tableau, IDEA, XBRL, and Weka. And with Connect, an easy-to-use homework and learning management solution that embeds learning science and award-winning adaptive tools to improve student outcomes, instructors receive an innovative course solution that includes cutting-edge content and assessment paired with assignments that help students build the skills they need to succeed. |
| accounting and data analytics: Guide to Audit Data Analytics and Audit Guide AICPA, 2018-01-09 This is a two-volume set. Guide to Data Analytics: Designed to facilitate the use of audit data analytics (ADAs) in the financial statement audit, this title was developed by leading experts across the profession and academia. The guide defines audit data analytics as the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for planning or performing the audit. Simply put, ADAs can be used to perform a variety of procedures to gather audit evidence. Each chapter focuses on an audit area and includes step-by-step guidance illustrating how ADAs can be used throughout the financial statement audit. Suggested considerations for assessing the reliability of data are also included in a separate appendix. Analytical Procedures 2017: Updated as of October 1, 2017, This book includes relevant guidance contained in applicable standards and other technical sources. The guide summarizes applicable requirements and practices and delivers how-to advice for handling analytical procedures used in audits. The included case study illustrates the use of analytical procedures in planning and substantive testing, utilizing trend analysis, ratio analysis, reasonableness testing, and regression analysis. The remaining appendixes include examples of financial ratios and a comparison of the key provisions of the risk assessment standards. |
| accounting and data analytics: ISE Introduction to Data Analytics for Accounting Vernon Richardson, Katie L. Terrell, Ryan A. Teeter, 2023-01-10 |
| accounting and data analytics: Statistical Techniques for Forensic Accounting Saurav K. Dutta, 2013-06-13 Master powerful statistical techniques for uncovering fraud or misrepresentation in complex financial data. The discipline of statistics has developed sophisticated, well-accepted approaches for identifying financial fraud and demonstrating that it is deliberate. Statistical Techniques for Forensic Accounting is the first comprehensive guide to these tools and techniques. Leading expert Dr. Saurav Dutta explains their mathematical underpinnings, shows how to use them properly, and guides you in communicating your findings to other interested and knowledgeable parties, or assessing others' analyses. Dutta is singularly well-qualified to write this book: he has been engaged as an expert in many of the world's highest-profile financial fraud cases, including Worldcom, Global Crossing, Cendant, and HealthSouth. Here, he covers everything professionals need to know to construct and conduct valid and defensible statistical tests, perform analyses, and interpret others' analyses. Coverage includes: exploratory data analysis to identify the Fraud Triangle and other red flags... data mining tools, usage, and limitations... statistical terms and methods applicable to forensic accounting... relevant uncertainty and probability concepts... Bayesian analysis and networks... statistical inference, sampling, sample size, estimation, regression, correlation, classification, prediction, and much more. For all forensic accountants, auditors, investigators, and litigators involved with corporate financial reporting; and for all students interested in forensic accounting and related fields. |
| accounting and data analytics: Data and Analytics in Accounting Ann C. Dzuranin, Guido Geerts, Margarita Lenk, 2023-12-25 |
| accounting and data analytics: Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) Cheng Few Lee, John C Lee, 2020-07-30 This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience. |
| accounting and data analytics: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| accounting and data analytics: Data Quality Jack E. Olson, 2003-01-09 Data Quality: The Accuracy Dimension is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it. Likewise, improving the accuracy of data in information systems is fast becoming a major goal as companies realize how much it affects their bottom line. Data profiling is a new technology that supports and enhances the accuracy of databases throughout major IT shops. Jack Olson explains data profiling and shows how it fits into the larger picture of data quality.* Provides an accessible, enjoyable introduction to the subject of data accuracy, peppered with real-world anecdotes. * Provides a framework for data profiling with a discussion of analytical tools appropriate for assessing data accuracy. * Is written by one of the original developers of data profiling technology. * Is a must-read for any data management staff, IT management staff, and CIOs of companies with data assets. |
| accounting and data analytics: Business Intelligence Jerzy Surma, 2011-03-06 This book is about using business intelligence as a management information system for supporting managerial decision making. It concentrates primarily on practical business issues and demonstrates how to apply data warehousing and data analytics to support business decision making. This book progresses through a logical sequence, starting with data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery, and finally the actual use of discovered knowledge. All examples are based on the most recent achievements in business intelligence. Finally this book outlines an overview of a methodology that takes into account the complexity of developing applications in an integrated business intelligence environment. This book is written for managers, business consultants, and undergraduate and postgraduates students in business administration. |
| accounting and data analytics: Benford's Law Mark J. Nigrini, 2012-03-09 A powerful new tool for all forensic accountants, or anyone whoanalyzes data that may have been altered Benford's Law gives the expected patterns of the digits in thenumbers in tabulated data such as town and city populations orMadoff's fictitious portfolio returns. Those digits, in unaltereddata, will not occur in equal proportions; there is a large biastowards the lower digits, so much so that nearly one-half of allnumbers are expected to start with the digits 1 or 2. Thesepatterns were originally discovered by physicist Frank Benford inthe early 1930s, and have since been found to apply to alltabulated data. Mark J. Nigrini has been a pioneer in applyingBenford's Law to auditing and forensic accounting, even before hisgroundbreaking 1999 Journal of Accountancy article introducing thisuseful tool to the accounting world. In Benford's Law, Nigrinishows the widespread applicability of Benford's Law and itspractical uses to detect fraud, errors, and other anomalies. Explores primary, associated, and advanced tests, all describedwith data sets that include corporate payments data and electiondata Includes ten fraud detection studies, including vendor fraud,payroll fraud, due diligence when purchasing a business, and taxevasion Covers financial statement fraud, with data from Enron, AIG,and companies that were the target of hedge fund short sales Looks at how to detect Ponzi schemes, including data on Madoff,Waxenberg, and more Examines many other applications, from the Clinton tax returnsand the charitable gifts of Lehman Brothers to tax evasion andnumber invention Benford's Law has 250 figures and uses 50 interestingauthentic and fraudulent real-world data sets to explain boththeory and practice, and concludes with an agenda and directionsfor future research. The companion website adds additionalinformation and resources. |
| accounting and data analytics: A First Course in Machine Learning Simon Rogers, Mark Girolami, 2016-10-14 Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/ |
| accounting and data analytics: Data Scientist Zacharias Voulgaris, 2014 Learn what a data scientist is and how to become one. As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist: Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity. Explore the different types of Data Scientists and the skillset each one has. Dig into what the role of the Data Scientist requires in terms of the relevant mindset, technical skills, experience, and how the Data Scientist connects with other people. Be a Data Scientist for a day, examining the problems you may encounter and how you tackle them, what programs you use, and how you expand your knowledge and know-how. See how you can become a Data Scientist, based on where you are starting from: a programming, machine learning, or data-related background. Follow step-by-step through the process of landing a Data Scientist job: where you need to look, how you would present yourself to a potential employer, and what it takes to follow a freelancer path. Read the case studies of experienced, senior-level Data Scientists, in an attempt to get a better perspective of what this role is, in practice. At the end of the book, there is a glossary of the most important terms that have been introduced, as well as three appendices - a list of useful sites, some relevant articles on the web, and a list of offline resources for further reading. |
| accounting and data analytics: HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) Harvard Business Review, 2018-03-13 Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes |
| accounting and data analytics: Data Analytics for Accounting Vernon J. Richardson, Data Analytics is changing the business world-data simply surrounds us! So much data is available to businesses about each of us-how we shop, what we read, what we buy, what music we listen to, where we travel, whom we trust, where we invest our time and money, and so on. Accountants create value by addressing fundamental business and accounting questions using Data Analytics-- |
| accounting and data analytics: Big data and analytics in accounting - e-Book AGOSTINI MARISA, ARKHIPOVA DARIA, 2023-04-28 Digital technologies such as big data analytics (BDA) are being increasingly used by businesses to create economic and societal value (Ferraris et al., 2019; Constantiou and Kallinikos, 2015; Günther et al., 2017; Rana et al., 2023). As a consequence, academic literature has emphasised their “disruptive potential” for enhancing corporate sustainability performance (Etzion and Aragon-Correa, 2015), creating more equal and inclusive society (Secundo et al., 2017), fostering optimal reallocation of underutilized resources (Etter et al., 2019) and enabling more participatory and democratic forms of governance (Neu et al., 2019; Ojala et al., 2019; Uldam, 2018). Conversely, the advocates of the critical approach have raised concerns about digital technologies related to privacy and security threats (La Torre et al., 2018), limitations of autonomy and freedom (Andrew and Baker, 2019), labour exploitation (Fuchs, 2010), lack of algorithmic accountability (Martin, 2019), pervasive worker control (Chai and Scully, 2019), and ecological footprint (Corbett, 2018; Lucivero, 2020). Hence, the magnitude and pervasiveness of ethical, social and environmental risks that emerge as a consequence of user data collection, storage and algorithmic processing are imposing additional responsibility upon data processing companies. To this end, the extant literature offers three main reasons for why large technology companies still lack accountability for these consequences. First, the problem resides in the inherent power asymmetries between the companies and individual users that pre-empt the latter from holding the former accountable for their wrongdoings (Rosenblat and Stark, 2016; West, 2019). Such quasi-monopolistic concentration of power in the hands of internet corporations is exerted not only vis-a-vis individual consumers but also other organisations (i.e., suppliers, competitors) whose business survival depends on the services of the large companies (Flyverbom et al., 2019). Second, regulatory efforts in the data economy often take place post hoc (Nunan and Di Domenico, 2017) and do not adequately address the contemporary issues of digitalization (Royakkers et al., 2018). Until recently, a self-regulatory regime prevailed in technology regulation based on “soft” voluntary standards and principles which the large companies developed for themselves. Finally, wrongful practices become pervasive to the extent that the other actors take them for granted and stop questioning them (Ananny and Crawford, 2018). As a result, companies find themselves in a “dual” position in which they simultaneously need to harness the potential of BDA to generate economic and societal value on the one hand, while at the same time are required establish an effective mechanism for ensuring accountability for the negative consequences of data utilization on the other. Hence, from the accounting perspective, this raises three important questions as to (1) whether accounting scholars can explain the emergent issues with BDA using established accounting theories, (2) whether and, if so, how the processing of BD results in calls for wider organisational accountability and greater regulatory oversight and (3) how the value of BDA can be assessed from a financial accounting standpoint. The present manuscript aims to address these questions. Chapter 1 “Emerging technologies in accounting” reviews technologies that underlie the use of BDA in accounting, provide definitions, discuss their interdependencies and explain differences between different technologies, illustrating their current and potential applications. In particular, new sources of big data and their characteristics will be discussed; different analytical approaches will be reviewed. The principal goal of this chapter is to establish a clear terminology and introduce key concepts that are fundamental for understanding the role of BDA in accounting. Chapter 2 “Peculiar and established theories framing studies of BDA in accounting” examines whether and how accounting literature has rooted BDA issues inside theoretical frameworks in order to formulate new concepts and models, to support the adoption of further methods and approaches, to explain and root the solutions used in practice. Chapter 3 “Data Regulations in the European Union” provides the most recent overview of the legal frameworks and regulatory developments in the European Union with regards to the data collection, use, storage, processing and sharing. Starting with the General Data Protection Regulation (GDPR) implementation in 2018, the European Union is taking a pioneer role in data-related regulations globally, imposes greater obligations, stricter rules and accountability frameworks. The chapter provides business and competitive context to explains the nature of the problem each regulatory initiative seeks to address, provides a general overview of the legal provisions in the context of the theoretical research in law, information systems and accounting and concludes by critical assessment of the effectiveness of the regulation – enforced or proposed – in reaching its goals and formulates a series of recommendations for potential improvement. Chapter 4 “Assessing the Value of Big Data and Analytics: Issues, Opportunities and Challenges” assesses the value of data that derives, rather than from inherent conditions, from the possibility of generating insights and the actual use of the same (Ferraris et al., 2019; Günther et al., 2017). “Conclusion” summarizes key research findings useful to provide answers to the above listed three research questions. |
| accounting and data analytics: Cost Accounting Karen C. Farmer, Amy J. Fredin, 2022 The text provides numerous discussions on how decision-makers are increasingly relying on data analytics to make decisions using accounting information. Accounting software systems collect vast amounts of data about a company's economic events as well as its suppliers and customers. Business decision-makers take advantage of this wealth of data by using data analytics to gain insights and therefore make more informed business decisions. Data analytics involves analyzing data, often employing both software and statistics, to draw inferences. As both data access and analytical software improve, the use of data analytics to support decisions is becoming increasingly common at virtually all types of companies.-- |
| accounting and data analytics: Loose Leaf for Data Analytics for Accounting Vernon Richardson, Ryan A. Teeter, Katie L. Terrell, 2022-01-25 Data Analytics is changing the business world--data simply surrounds us, which means all accountants must develop data analytic skills to address the needs of the profession in the future. Data Analytics for Accounting 3e is designed to prepare your students with the necessary tools and skills they need to successfully perform data analytics through a conceptual framework and hands-on practice with real-world data. Using the IMPACT Cycle, the authors provide a conceptual framework to help students think through the steps needed to provide data-driven insights and recommendations. Once students understand the foundation of providing data-driven insights, they are then provided hands-on practice with real-world data sets and various data analysis tools which students will use throughout the rest of their career. The data analysis tools are structured around two tracks--the Microsoft track (Excel, Power Pivot, and Power BI) and a Tableau track (Tableau Prep and Tableau Desktop). Using multiple tools allows students to learn which tool is best suited for the necessary data analysis, data visualization, and communication of the insights gained. Data Analytics for Accounting 3e is a full-course data analytics solution guaranteed to prepare your students for their future careers as accountants. |
| accounting and data analytics: Big Data in Practice Bernard Marr, 2016-05-02 The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter |
| accounting and data analytics: Applied Predictive Modeling Max Kuhn, Kjell Johnson, 2013-05-17 Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. |
Data Analytics for Accounting - McGraw Hill
However, we do emphasize seven skills that we believe analytic-minded accountants should have: 1. An analytics mindset—recognize when and how Data Analytics can address business …
Big data and analytics: the impact on the accountancy …
Applying analytics to big data creates many opportunities for businesses to gain greater insight, predict future outcomes and automate non-routine tasks. It also provides opportunities for the …
Data analytics impacts in the field of accounting
Based on the information collected from scholarly sources, data analytics, and business intelligence is helpful for business organizations in the improvement of their accounting …
Data Analytics in Accounting - Drake University
Richardson & Watson (2021) propose revolutionizing accounting curriculum with data analytics. Blix, Edmonds, and Sorensen (2021) reviewed current audit textbooks on how well they have …
ACCOUNTING INFORMATION SYSTEMS AND DATA …
Describe the benefits of using data analytics. Understand the impact of data analytics on accounting. Recognize accounting information systems used globally. Recognize current tools …
Microsoft Word - ACCT 4710 2024- Data Analytics and
Apply and understand big data issues and utilize data mining, data modeling, data analysis and data visualization techniques to solve accounting and business related issues. Synthesize …
Introduction to Data & Analytics for Finance and Accounting …
Finance and audit professionals and any other professionals keen to learn how data analytics can help to drive better decision-making. Hands on exercises allow participants to compare and …
University of Arkansas, - etextbook.to
From learning to ask the right questions to interpreting and presenting results, Introduction to Data Analytics for Accounting, 2e fosters critical thinking while exploring data analytics skills.
Transforming Financial Accounting with Data Analytics
How can Data Analytics support this approach? My Assignment Variety – Old vs. New. What are some of the current liabilities reported by companies in the airline industry? includes advanced …
Data Analytics for Accounting - McGraw Hill Education
Accountants create value by addressing fundamental business and accounting questions using Data Analytics. All accountants must develop data analytic skills to address the needs of the …
Big Data Analytics for Business Intelligence in Accounting and …
By using data analytics procedures, accountants and auditors can produce high- quality, statistical forecasts that help them understand and identify risks relating to the frequency and value of …
Accounting BBA with Data Analytics Concentration
The Bachelor of Business Administration in Accounting with an optional Concentration in Data Analytics is open to all accounting majors and is designed to develop skills in problem-solving, …
Data Analytics for Accounting
Data Analytics for Accounting, 2e also emphasizes the various data analysis tools students will use throughout the rest of their career—Microsoft Excel, Microsoft Access (including SQL), …
Impacts of big data on accounting
Jan 20, 2022 · The trend of big data analytics in accounting facilitated by growth in computing power, ability to capture data and utilize various types of data from diverse sources presents …
Analytics in finance and accountancy - ACCA Global
The opportunity for the accountancy and finance professional to use data analytics to support faster and better decision making in the finance function and beyond is clear.
Big Data in Accounting: An Overview - NPTU
They consider the changing nature of accounting records and the incorporation of nontraditional sources of data into the accounting and auditing domains, as well as the need for changes in …
Accounting Data Analytics, CERT
The Accounting Data Analytics Graduate Certificate is designed to develop leaders who understand (1) how to apply data analytics in a variety of accounting and business contexts, …
Data Analytics for Accounting - McGraw Hill
Specific chapter changes for Data Analytics for Accounting, 2nd Edition, are as follows: Chapter 1 Updated the opening vignette and statistics on Alibaba sales and use of e-commerce. Updated …
Introduction to Data Analytics for Accounting
From learning to ask the right questions to interpreting and presenting results, Introduction to Data Analytics for Accounting, 2e fosters critical thinking while exploring data analytics skills.
ANALYTICS IN FINANCE AND ACCOUNTANCY - ACCA Global
In this report we offer insight as to the skills needed and reflect upon the need for accountants to understand the problem and articulate the analysis, as much as their need for skills relevant to …
Data Analytics for Accounting - McGraw Hill
However, we do emphasize seven skills that we believe analytic-minded accountants should have: 1. An analytics mindset—recognize when and how Data Analytics can address business …
Big data and analytics: the impact on the accountancy …
Applying analytics to big data creates many opportunities for businesses to gain greater insight, predict future outcomes and automate non-routine tasks. It also provides opportunities for the …
Data analytics impacts in the field of accounting
Based on the information collected from scholarly sources, data analytics, and business intelligence is helpful for business organizations in the improvement of their accounting …
Data Analytics in Accounting - Drake University
Richardson & Watson (2021) propose revolutionizing accounting curriculum with data analytics. Blix, Edmonds, and Sorensen (2021) reviewed current audit textbooks on how well they have …
ACCOUNTING INFORMATION SYSTEMS AND DATA …
Describe the benefits of using data analytics. Understand the impact of data analytics on accounting. Recognize accounting information systems used globally. Recognize current tools …
Microsoft Word - ACCT 4710 2024- Data Analytics and
Apply and understand big data issues and utilize data mining, data modeling, data analysis and data visualization techniques to solve accounting and business related issues. Synthesize …
Introduction to Data & Analytics for Finance and …
Finance and audit professionals and any other professionals keen to learn how data analytics can help to drive better decision-making. Hands on exercises allow participants to compare and …
University of Arkansas, - etextbook.to
From learning to ask the right questions to interpreting and presenting results, Introduction to Data Analytics for Accounting, 2e fosters critical thinking while exploring data analytics skills.
Transforming Financial Accounting with Data Analytics
How can Data Analytics support this approach? My Assignment Variety – Old vs. New. What are some of the current liabilities reported by companies in the airline industry? includes advanced …
Data Analytics for Accounting - McGraw Hill Education
Accountants create value by addressing fundamental business and accounting questions using Data Analytics. All accountants must develop data analytic skills to address the needs of the …
Big Data Analytics for Business Intelligence in Accounting …
By using data analytics procedures, accountants and auditors can produce high- quality, statistical forecasts that help them understand and identify risks relating to the frequency and value of …
Accounting BBA with Data Analytics Concentration
The Bachelor of Business Administration in Accounting with an optional Concentration in Data Analytics is open to all accounting majors and is designed to develop skills in problem-solving, …
Data Analytics for Accounting
Data Analytics for Accounting, 2e also emphasizes the various data analysis tools students will use throughout the rest of their career—Microsoft Excel, Microsoft Access (including SQL), …
Impacts of big data on accounting
Jan 20, 2022 · The trend of big data analytics in accounting facilitated by growth in computing power, ability to capture data and utilize various types of data from diverse sources presents …
Analytics in finance and accountancy - ACCA Global
The opportunity for the accountancy and finance professional to use data analytics to support faster and better decision making in the finance function and beyond is clear.
Big Data in Accounting: An Overview - NPTU
They consider the changing nature of accounting records and the incorporation of nontraditional sources of data into the accounting and auditing domains, as well as the need for changes in …
Accounting Data Analytics, CERT
The Accounting Data Analytics Graduate Certificate is designed to develop leaders who understand (1) how to apply data analytics in a variety of accounting and business contexts, …
Data Analytics for Accounting - McGraw Hill
Specific chapter changes for Data Analytics for Accounting, 2nd Edition, are as follows: Chapter 1 Updated the opening vignette and statistics on Alibaba sales and use of e-commerce. Updated …
Introduction to Data Analytics for Accounting
From learning to ask the right questions to interpreting and presenting results, Introduction to Data Analytics for Accounting, 2e fosters critical thinking while exploring data analytics skills.
