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Accounting with Data Analytics: A Critical Analysis of Current Trends
Author: Dr. Anya Sharma, CPA, CMA, PhD in Accounting Information Systems
Publisher: Journal of Accountancy (AICPA), a leading publication in the accounting field known for its rigorous peer-review process and high editorial standards.
Editor: David Miller, CPA, CGMA, with over 20 years of experience in accounting and finance, specializing in the integration of technology in accounting practices.
Keywords: accounting with data analytics, data analytics in accounting, accounting data analytics, big data accounting, predictive accounting, audit analytics, financial analytics, accounting automation, data-driven accounting, AI in accounting
Abstract: This analysis explores the transformative impact of accounting with data analytics on current accounting trends. We examine how the integration of data analytics is revolutionizing various aspects of the accounting profession, from auditing and financial reporting to fraud detection and strategic decision-making. The paper also critically assesses the challenges and opportunities presented by this burgeoning field, including data security, skills gaps, and ethical considerations.
1. The Rise of Data-Driven Accounting
The accounting profession is undergoing a significant transformation, fueled by the exponential growth of data and advancements in data analytics. Accounting with data analytics is no longer a niche area but a core competency for accountants at all levels. Businesses are generating unprecedented volumes of data – transactional data, operational data, customer data, and market data – all of which hold immense potential for improving accounting processes and providing valuable business insights. Traditional accounting methods, which often relied on manual processes and limited data analysis, are being replaced by sophisticated data-driven approaches.
2. Applications of Accounting with Data Analytics
The applications of accounting with data analytics are vast and expanding rapidly. Some key areas include:
Auditing: Accounting with data analytics enables auditors to perform more efficient and effective audits. Data analytics techniques, such as anomaly detection and predictive modeling, can identify potential risks and areas requiring further investigation, significantly reducing audit time and improving audit quality.
Financial Reporting: Data analytics streamlines financial reporting processes, allowing for faster and more accurate reporting. It helps identify inconsistencies, outliers, and potential errors, enhancing the reliability and transparency of financial statements.
Fraud Detection: Accounting with data analytics plays a crucial role in identifying and preventing fraud. By analyzing large datasets, accountants can detect anomalies and patterns indicative of fraudulent activities, providing early warnings and preventing significant financial losses.
Predictive Analytics: Accounting with data analytics allows for predictive modeling, enabling businesses to forecast future financial performance and make proactive decisions. This can include predicting cash flow, sales revenue, and other key financial metrics.
Tax Compliance: Data analytics assists in streamlining tax processes, ensuring accurate tax filings and reducing the risk of penalties. It can automate complex tax calculations and identify potential tax optimization opportunities.
Cost Management: Analyzing cost data using accounting with data analytics helps organizations identify cost-saving opportunities and improve efficiency. This can include identifying areas of waste, optimizing resource allocation, and improving pricing strategies.
3. Challenges and Opportunities
While the benefits of accounting with data analytics are undeniable, there are also challenges to overcome. These include:
Data Security and Privacy: Protecting sensitive financial data is paramount. Robust data security measures are crucial to prevent data breaches and ensure compliance with regulations like GDPR and CCPA.
Skills Gap: A significant skills gap exists in the accounting profession, with a shortage of professionals possessing the necessary data analytics skills. Education and training initiatives are essential to bridge this gap.
Data Quality: The accuracy and reliability of data analytics depend on the quality of the underlying data. Data cleansing and validation are critical steps to ensure the integrity of the analysis.
Ethical Considerations: The use of data analytics in accounting raises ethical concerns, particularly regarding bias in algorithms and the potential for misuse of data. Clear ethical guidelines and best practices are necessary.
Cost of Implementation: Implementing data analytics solutions can be expensive, requiring investments in software, hardware, and training. Cost-benefit analysis is crucial to ensure a return on investment.
4. The Future of Accounting with Data Analytics
The future of accounting with the integration of data analytics is bright. We anticipate further advancements in areas like artificial intelligence (AI) and machine learning (ML), which will further automate accounting processes and enhance the analytical capabilities of accounting professionals. The increasing availability of cloud-based data analytics platforms will also make these tools more accessible to smaller firms. The demand for professionals skilled in accounting with data analytics will continue to grow, creating numerous opportunities for career advancement.
Conclusion:
Accounting with data analytics is transforming the accounting profession, offering significant benefits in terms of efficiency, accuracy, and insights. While challenges remain, the opportunities presented by this powerful combination are immense. Addressing the skills gap, ensuring data security, and establishing ethical guidelines are crucial steps to fully realize the potential of accounting with data analytics and shaping a future where data-driven insights guide better business decisions.
FAQs:
1. What are the most in-demand data analytics skills for accountants? SQL, Python, R, data visualization tools (Tableau, Power BI), and understanding of statistical methods are highly sought-after.
2. How can I improve my data analytics skills as an accountant? Enroll in online courses, pursue relevant certifications (e.g., CPA with data analytics specialization), and seek opportunities to work on data-intensive projects.
3. What software tools are commonly used in accounting with data analytics? Popular tools include Alteryx, Tableau, Power BI, Qlik Sense, and various programming languages like Python and R.
4. How does accounting with data analytics improve audit quality? It enables auditors to analyze larger datasets, identify anomalies, and assess risks more efficiently, leading to more comprehensive and reliable audits.
5. What are the ethical considerations of using AI in accounting? Addressing potential biases in algorithms, ensuring data privacy, and maintaining transparency in decision-making processes are crucial.
6. How can small accounting firms benefit from data analytics? Cloud-based solutions offer cost-effective access to powerful data analytics tools, allowing smaller firms to compete with larger organizations.
7. What is the difference between accounting with data analytics and traditional accounting? Traditional accounting relies heavily on manual processes and limited data analysis, while accounting with data analytics leverages technology and sophisticated analytical methods.
8. What is the future of jobs in accounting with data analytics? The demand for professionals skilled in this area is expected to continue to grow, creating many career opportunities.
9. How can I find relevant job opportunities in accounting with data analytics? Utilize job boards, network with professionals in the field, and tailor your resume and cover letter to highlight your data analytics skills.
Related Articles:
1. "The Impact of Big Data on Financial Reporting": This article explores how the exponential growth of data is transforming financial reporting practices and the role of data analytics in ensuring accuracy and transparency.
2. "Data Analytics in Fraud Detection: A Case Study": A case study illustrating how data analytics techniques were used to identify and prevent a major fraud incident within a large corporation.
3. "Predictive Accounting: Forecasting Financial Performance with Data Analytics": This article delves into the application of predictive modeling in forecasting key financial metrics and supporting strategic decision-making.
4. "Building a Data-Driven Audit Strategy": A practical guide to implementing data analytics in audit processes, outlining best practices and key considerations.
5. "The Role of Artificial Intelligence in Accounting": An exploration of how AI and machine learning are changing the accounting landscape, automating tasks and enhancing analytical capabilities.
6. "Addressing the Skills Gap in Accounting with Data Analytics": This article discusses strategies for bridging the skills gap through education, training, and professional development initiatives.
7. "Data Security and Privacy in Accounting with Data Analytics": A focus on the importance of data security and compliance with relevant regulations in the context of data analytics.
8. "Ethical Considerations in the Use of Data Analytics in Accounting": An examination of the ethical implications of data analytics, addressing issues such as bias, transparency, and accountability.
9. "Cost-Benefit Analysis of Implementing Data Analytics in Accounting Firms": This article provides a framework for evaluating the return on investment of implementing data analytics solutions in accounting firms of various sizes.
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| accounting with 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 with data analytics: Analytics and Big Data for Accountants Jim Lindell, 2020-10-29 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 with 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 with 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 with 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 with 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 with 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. |
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| accounting with 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 with 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 with 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 with 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 with 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 with 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 with 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 with 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 with 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 with 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. |
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| accounting with 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 with data analytics: Data Science Field Cady, 2020-12-30 Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the “business side” of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring them When Big Data is the best approach to use for a project, and when it actually ties analysts’ hands Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems How many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation. |
| accounting with 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 with 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 with data analytics: Digital Transformation in Accounting Richard Busulwa, Nina Evans, 2021-05-30 Digital Transformation in Accounting is a critical guidebook for accountancy and digital business students and practitioners to navigate the effects of digital technology advancements, digital disruption, and digital transformation on the accounting profession. Drawing on the latest research, this book: Unpacks dozens of digital technology advancements, explaining what they are and how they could be used to improve accounting practice. Discusses the impact of digital disruption and digital transformation on different accounting functions, roles, and activities. Integrates traditional accounting information systems concepts and contemporary digital business and digital transformation concepts. Includes a rich array of real-world case studies, simulated problems, quizzes, group and individual exercises, as well as supplementary electronic resources. Provides a framework and a set of tools to prepare the future accounting workforce for the era of digital disruption. This book is an invaluable resource for students on accounting, accounting information systems, and digital business courses, as well as for accountants, accounting educators, and accreditation / advocacy bodies. |
| accounting with data analytics: Data Analytics Warren W. Stippich, 2016 |
| accounting with 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 with 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 with 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 with 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 with 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. |
| accounting with 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 with data analytics: Business Information Systems Witold Abramowicz, Rafael Corchuelo, 2019-06-18 The two-volume set LNBIP 353 and 354 constitutes the proceedings of the 22nd International Conference on Business Information Systems, BIS 2019, held in Seville, Spain, in June 2019. The theme of the BIS 2019 was Data Science for Business Information Systems, inspiring researchers to share theoretical and practical knowledge of the different aspects related to Data Science in enterprises. The 67 papers presented in these proceedings were carefully reviewed and selected from 223 submissions. The contributions were organized in topical sections as follows: Part I: Big Data and Data Science; Artificial Intelligence; ICT Project Management; and Smart Infrastructure. Part II: Social Media and Web-based Systems; and Applications, Evaluations and Experiences. |
| accounting with 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 with 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 with data analytics: Big Data Viktor Mayer-Schönberger, Kenneth Cukier, 2013 A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large. |
| accounting with data analytics: Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing Singh, Amandeep, 2021-06-18 The availability of big data, low-cost commodity hardware, and new information management and analytic software have produced a unique moment in the history of data analysis. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue, and profitability especially in digital marketing. Data plays a huge role in understanding valuable insights about target demographics and customer preferences. From every interaction with technology, regardless of whether it is active or passive, we are creating new data that can describe us. If analyzed correctly, these data points can explain a lot about our behavior, personalities, and life events. Companies can leverage these insights for product improvements, business strategy, and marketing campaigns to cater to the target customers. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing aids understanding of big data in terms of digital marketing for meaningful analysis of information that can improve marketing efforts and strategies using the latest digital techniques. The chapters cover a wide array of essential marketing topics and techniques, including search engine marketing, consumer behavior, social media marketing, online advertising, and how they interact with big data. This book is essential for professionals and researchers working in the field of analytics, data, and digital marketing, along with marketers, advertisers, brand managers, social media specialists, managers, sales professionals, practitioners, researchers, academicians, and students looking for the latest information on how big data is being used in digital marketing strategies. |
| accounting with 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 with data analytics: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies Erik Brynjolfsson, Andrew McAfee, 2014-01-20 The big stories -- The skills of the new machines : technology races ahead -- Moore's law and the second half of the chessboard -- The digitization of just about everything -- Innovation : declining or recombining? -- Artificial and human intelligence in the second machine age -- Computing bounty -- Beyond GDP -- The spread -- The biggest winners : stars and superstars -- Implications of the bounty and the spread -- Learning to race with machines : recommendations for individuals -- Policy recommendations -- Long-term recommendations -- Technology and the future (which is very different from technology is the future). |
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Beyond Accounting LLC is built to help businesses of many sizes, from start-ups to mid-sized established companies, manage their financial and accounting back office.
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Bridging the gap between CFO and accounting services with our strategic suite of CFO-level services intended to turn major ambitions into manageable action plans. Customized …
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They are easy to use, seamless tax preparation and always available when you need documents for things like closing on a home. I appreciate their attention to detail and their help when I …
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We are accounting and bookkeeping experts that specialize in providing financial reconciliations, monthly financial statement creation, and transaction processing for small to medium-sized …
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Accounting is known as the language of business. Through a series of steps known as accounting cycle, it gathers information about business transactions, and collates and summarizes them …
