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Rochak answered on
Sep 23 2022
INVESTIGATION REPORT
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Project Title:
" The Impact of data analytics on the role of managerial accounting."
Table of Contents
1 Chapter 1 6
1.1 Introduction 6
1.2 Background 7
1.3 Problem Statement 9
1.4 Research Questions 10
1.5 Objective 10
1.6 Significance of Study 11
1.7 Scope 12
1.8 Definitions 12
1.8.1 What is management accounting? 12
1.8.2 Descriptive analytics 13
1.8.3 Predictive analytics 13
1.8.4 Prescriptive analytics 13
2 Chapter 2 14
2.1 Changing the role of managerial accounting 14
2.1.1 Decision-making process 15
2.1.2 Impact of big data on the business enterprise system 15
2.1.3 Experimental research in management accounting 16
2.1.4 Managerial Accounting Data Analytics (MADA) framework 17
2.1.5 Financial perspective 19
3 Chapter 3 21
3.1 Research Methodology 21
3.1.1 Research Design 21
3.1.2 Research Approach 22
3.1.3 Data collection method and tools 22
3.1.4 Data analysis 23
3.1.5 Validity 24
Chapter 4: Data Presentation and analysis 25
4.1. Introduction 25
4.1.1. Questionnaire 25
4.1.2 Demographic Profile 29
4.1.3 Age of Respondents 30
4.1.4 Working Status 32
4.1.5 Positions held within the company 33
Table 4.1.5: Positions held within the company of the Respondent 33
Exhibit 4.1.5: Pie Chart of the Positions held within the company of the respondents 34
4.2 Analysis of the Data collected 35
4.3 Conclusion 51
Chapter 5: Findings and discussion 52
5.1. Introduction 52
5.2 Advantages
ought by data analysis and big data to the accounting work 52
5.2.1 Streamline accounting workflow 53
5.2.2 Better use of accounting functions 53
5.2.3 Optimize the way the business operates 53
5.3 Impact of data analysis on managerial accounting 54
5.4 Data analytics challenges in managerial accounting 55
5.4.1 Cu
ent situation of account work is difficult to change 55
5.4.2 Accounting data management 56
5.4.2 Accounting requires security 56
5.5 Data Findings 57
5.6 Conclusion 58
Chapter 6: Suggestions, Conclusion and recommendations 60
6.1 Introduction 60
6.2 Suggestion 60
6.3 Conclusion 63
6.4 Recommendation 64
6.5 Limitations 65
6.6 Future Research 66
References 68
Tables
Table 4.2.1: Daily activity involves accounting
Table 4.1.2: Gender Profile of the Respondents
Table 4.1.3: Age Profile of the Respondent
Table 4.1.4: Working status of the Respondent
Table 4.2.2: Data analysis in accounting is increasing
Table 4.2.3: Data analytics makes accounting easier
Table 4.2.4: Managerial accounting is complex
Table 4.2.5: Managerial accounting has become easy to understand with the increasing use of data analysis
Table 4.2.6: Technology is an advantage to the accounting work
Table 4.2.7: Advantages of data analysis to managerial accounting
Table 4.2.7: Challenges in data analysis
Table 4.2.8: Data analysis is done using software
Table 4.2.9: Pace of accepting data analysis
ig data
Table 4.2.10: Big data helpful in decision making
Table 4.2.11: Changes in the implementation of big data
Exhibits
Exhibit 4.1.2: Pie Chart of gender profile of the respondents
Exhibit 4.1.3: Pie Chart of the Age of the respondents
Exhibit 4.1.4: Pie Chart of the Working status of the respondents…………………………
Exhibit 4.2.1: Graph of the Positions held within the company of the respondents
Exhibit 4.2.7: Graph of challenges
Exhibit 4.2.8: Graph for data analysis is done using software……………………………...
Exhibit 4.2.9: Graph of Pace of accepting data analysis
ig data
Exhibit 4.2.10: Graph of Big data helpful in decision making……………………………..
Exhibit 4.2.11: Graph of Changes in the implementation of big data
The Impact of data analytics on the role of managerial accounting.
1 Chapter 1
1.1 Introduction
Managerial Accounting is a
anch of accounting that supports the company's management in planning, decision-making, monitoring, and analysis. The effective use of this tool by management can ensure profitable growth and business optimization. To make business decisions, business owners or managers need appropriate financial and economic information and structure according to their needs and analysis of information resources and business results.
The nature of management accountants' responsibility is evolving from simply covering collective historical worth to conjointly together with structure performance measuring and providing management with call-connected data. company information systems equivalent to enterprise resource coming up with (ERP) systems have provided management accountants with each swollen information storage power and increased procedure power. With huge data extracted from both internal and external data sources, management accountants cu
ently may utilize data analytics techniques to answer the queries including what is going on (descriptive analytics), what's going to happen (predictive analytics), and what's the optimized resolution (prescriptive analytics). However, analysis shows that the character and scope of social control accounting have been blankly modified, and management accountants use principally descriptive analytics, some prophetic analytics, and a bare minimum of prescriptive analytics.
Over the years, the role of management accountants has significantly changed. Serving the purpose of assisting and participating in decision-making with management, modern management accountants work from four aspects: to participate in strategic cost management for achieving long-term goals; to implement management and operational control for corporate performance measures; to plan for internal cost activity; and to prepare financial statements (Brands, 2015). As business competition has increased tangentially with technology development, the scope of managerial accounting has also expanded from historical value reporting to more real-time reporting and predictive reporting (Cokins, 2013).
The purpose of this research is to discuss the potential impact of data analytics, big data, and enterprise systems on managerial accounting and to provide a framework that implements business analytics techniques into the enterprise system for measuring company performance using the balanced scorecard (BSC) framework from a management accounting perspective. While some literature describes the impact of business analytics on management accounting (Nielsen, 2015; Silvi et al., 2010), little research discusses using business analytics for measuring a company's performance in an enterprise system environment (Nielsen et al., 2014).
1.2 Background
Evolving from its traditional emphasis on financially oriented decision analysis and budgetary control, modern managerial accounting encompasses a more strategic approach that emphasizes the identification, measurement, and management of the key financial and operational drivers of shareholder value (Ittner and Larcker, 2001). The goal of management accounting is to provide managers with operational and financial accounting information. Management accountants serve the role of participating in strategic cost management for achieving long-term goals; implementing management and operational control for corporate performance measurement; planning for internal cost activity; and preparing financial statements (Brands, 2015). To support this intended role, the main obligations of management accountants can be classified into:
(1) Preparing financial statements.
(2) Measuring the company's performance; and
(3) Providing decision-related information (Cokins, 2013).
With ERP systems and powerful business analytic tools that provide enterprises with the ability to interpret and analyze various types of data (such as internal/external, structured/unstructured, and financial/nonfinancial), it is crucial for management accountants to adjust their responsibility to help companies gain competitive advantage (Nielsen, 2015). In the preparation of financial statements, management accountants use accumulated historical values to report the financial situation of the company. However, in a business world that requires more timely and relevant information, financial statements usually are not an ideal source of information for decision-making by management as they are backwards-looking, reporting on past events rather than providing the forward-looking data needed for running the business. Modern management accountants assist management with measuring firm performance from internal data and providing decision-related information from both internal and external data. Not only should management accountants provide descriptive reports to answer questions about prior events, but they also need to make predictions including consequences for uncertainty and risk in decisions (Nielsen, 2015).
To fulfil these challenging tasks that help the business stay competitive, management accountants now can use business analytics tools to conduct prescriptive analysis to support decision-makers against uncertainties. For example, an optimization model could allow accountants in a manufacturing company to choose among different raw material vendors which could reduce cost and boost revenue (Taleizadeh et al., 2015). It is suggested that management accountants should transgress the boundaries of management accounting and interact with non-accountants to solve practical problems (Birnberg, 2009). Cokins (2013) highlights seven trends that are occu
ing in management accounting:
(1) Expansion from product to channel and customer profitability analysis.
(2) Management accounting's expanding role with enterprise performance management (EPM)
(3) The shift to predictive accounting
(4) Business analytics embedded in EPM methods
(5) Coexisting and improved management accounting methods
(6) Managing information technology and shared services as a business
(7) The need for better skills and competency with behavioural cost management.
In summary, management accounting has
oadened its domain from conventional financial reporting to also including performance measurement and strategic decision making. Specifically, management accounting has extended its traditional focus to include identifying the drivers of financial performance, both internal and external to the business. New and revolutionary non-financial metrics and approaches have been added to management accounting functions, with an impact that is still being studied by academics and practitioners (Silvi et al., 2010).
1.3 Problem Statement
This research contributes to the literature in several ways. First, this research work discusses the impact of business data analytics on managerial accounting from an enterprise system perspective. Although some researchers have proposed a BSC framework for management accountants to apply business analytics (Nielsen, 2015; Silvi et al., 2010), few have examined this issue within the enterprise systems context. Second, this study discusses the impact Managerial Accounting Data Analytics (MADA) framework that incorporates the BSC framework for management accountants to utilize data analytics for corporate performance measurement. Lastly, attributes related to the implementation of a MADA framework (i.e., business intelligence context, data quality and integrity) are discussed to build the connection between the MADA framework and modern business practice.
1.4 Research Questions
The four main questions that describe the impact of data analytics on managerial accounting will be answered after the completion of this research.
Q 1: The role of managerial accounting on financially oriented decision analysis and budgetary control using data analytics to a more strategic approach that emphasizes identifying, measuring, and managing the key financial and operational drivers of shareholder value.
Q2: The impact of poor-quality data has been identified in numerous academic studies and these concerns are magnified with big data.
Q3: The most basic challenge of big data applications with analytics is the exploration of big data and the subsequent extraction of useful information for the analytical application.
Q4: Data security is an enterprise-wide concern, with controls and procedures established by the IT department and the management accountant needs to ensure that the controls and procedures are followed.
1.5 Objective
It is important to have a clear objective as to why this research should be ca
ied out. Based on the research problem definition given above, the goal of this research is given as to offer empirical evidence regarding the influence of Big Data and analytics on management control systems. This study aims to answer the central question of this research by answering several sub-questions first. By acknowledging that the objective mentioned in the research objective part of this thesis is the expected output of this research, it is important to answer the research question to achieve the targeted output in the end. The central question of this research is constructed as follows:
“What is the influence of Big Data and analytics on Managerial Accounting systems?”
1.6 Significance of Study
This study is aimed to be scientific and practical relevance. Even though data is becoming the new form of capital and source of competitive advantage, the concept of Big Data and analytics is yet a newly emerging and challenging concept for many to grasp. Finding prior research is thus not easy because hardly any empirical studies have been conducted on this topic. Existing literature on the subject is mainly aimed at theorizing the concept and analyzing some of the different analytical methods and tools which can be applied to Big Data, as well as identifying opportunities and challenges that emerge in simultaneous Big Data application (Frizzo-Barker et al., 2016).
For that reason, this research aims to provide empirical evidence and understanding of how Big Data and analytics may influence Management Control Systems within organizations. By doing so, this study contributes to the literature in the areas of Big Data and management control systems. Furthermore, from a practical perspective, the relevance of this research is to provide some applied insights to decision makers vis-àvis the control challenges that may be expected and the types of controls these require if an organization decides to implement Big Data and analytics information technology.
1.7 Scope
Purpose – The purpose of this paper is to speculate on the potential of combining management accounting with the idea of data analytics. Or more specifically how the three stages of business analytics can be used for combining the balanced scorecard with other related accounting concepts.
Design/methodology/approach – The work is based on research literature and surveys provided by researchers and consultants within business analytics and the balanced scorecard.
Practical implications – The research provides information and consideration on how to move from a simple descriptive approach to the more advanced level of prescriptive level in business analytics by using the arsenal of different quantitative tools that are available. Subsequently, it provides some reflections on the skills that future accountants must possess to be able to fulfil their future roles.
Originality/value – The value of this paper is two-fold: first, it shows that relying on business analytics and its systematic methodological approach will open up new possibilities for management accountants; second, it shows that integrating the principles of business analytics and the BSC framework it is possible to develop a comprehensive data-driven approach to dynamic performance management relevant for decision making.
1.8 Definitions
1.8.1 What is management accounting?
National Association of Accountants of the United States issued its first definition in 1981 with the document entitled Defining Management Accounting, in which management accounting is defined as "... The process of identifying, measuring, accumulating, analysing, preparing, interpreting, and communicating financial information is used by management to plan, evaluate, and control the organization and ensure proper use and management responsibility resources. Management Accounting also includes the preparation of financial reports for nonmanagement groups such as shareholders, creditors, regulatory agencies and tax authorities "
1.8.2 Descriptive analytics
Descriptive analytics answers the question as to what happened. It is the most common type of analytics used by businesses (IBM, 2013) and is typically characterized by descriptive statistics, Key Performance Indicators (KPIs), dashboards, or other types of visualizations (Dilla et al., 2010). Descriptive analytics summarize what has happened and which also form the basis of many continuous monitoring alert systems, where transactions are compared to benchmarks and thresholds are established from ratio and trend analysis of historical data.
1.8.3 Predictive analytics
Predictive analytics is the next step taken with the knowledge acquisition from descriptive analytics (Bertsimas and Kallus, 2014) and answers the question of what could happen (IBM, 2013). It is characterized by predictive and probability models, forecasts, statistical analysis and scoring models. Predictive models use historical data accumulated over time to make calculations of probable future events. Most businesses use predominantly descriptive analytics and are just beginning to use predictive analytics (IBM, 2013).
1.8.4 Prescriptive analytics
Prescriptive analytics (Bertsimas and Kallus, 2014; Holsapple et al., 2014; IBM, 2013; Ayata, 2012) answers the question of what should be done given the descriptive and predictive analytics results. Prescriptive analytics may be described as an optimization approach. Prescriptive analytics go beyond descriptive and predictive by recommending one or more solutions and showing the likely outcome of each.
2 Chapter 2
2.1 Changing the role of managerial accounting
Evolving from its traditional emphasis on financially oriented decision analysis and budgetary control, modern managerial accounting encompasses a more strategic approach that emphasizes the identification, measurement, and management of the key financial and operational drivers of shareholder value (Ittner and Larcker, 2001). The goal of management accounting is to provide managers with operational and financial accounting information. Management accountants serve the role of participating in strategic cost management for achieving long-term goals; implementing management and operational control for corporate performance measurement; planning for internal cost activity; and preparing financial statements (Brands, 2015). To support this intended role, the main obligations of management accountants can be classified into
(1) Preparing financial statements
(2) Measuring the company's performance
(3) Providing decision-related information (Cokins, 2013).
With ERP systems and powerful business analytic tools that provide enterprises with the ability to interpret and analyze various types of data (such as internal/external, structured/unstructured and financial/nonfinancial), it is crucial for management accountants to adjust their responsibility to help companies gain competitive advantage (Nielsen, 2015). In the preparation of financial statements, management accountants use accumulated historical values to report the financial situation of the company. However, in a business world that requires more timely and relevant information, financial statements usually are not an ideal source of information for decision-making by management as they are backwards-looking, reporting on past events rather than providing the forward-looking data needed for running the business. Modern management accountants assist management with measuring firm performance from internal data and providing decision-related information from both internal and external data. Not only should management accountants provide descriptive reports to answer questions about prior events, but they also need to make predictions including consequences for uncertainty and risk in decisions (Nielsen, 2015).
2.1.1 Decision-making process
The decision-making process is a process of creating value for the business, through planning, controlling, and evaluating performance. Business value results from good management decisions. Quality decision making can only consistently occur by reliance on valuable information. So, the relevance of managerial accounting is crucial for the success of a manager and the success of a company or organization.
2.1.2 Impact of big data on the business enterprise system
Big data could be regarded as data sets so large or unstructured that they cannot be processed and analyzed easily using most database management systems and software programs (Wa
en et al., 2015). Big data in its entirety can originate from traditional transaction systems as well as from new unstructured sources such as emails, audio files, internet click streams, social media, news media, sensor recordings, videos, and RFID tags (Zhang et al., 2015). Big data has become characterized by four qualities or the four V's: immense Volume, high Velocity,
oad Variety, and uncertain Veracity (Laney, 2001; IBM, 2012). Historically, business and accounting data reported transactions and other structured data, such as orders, sales, purchase orders, shipments, receivables, personnel information, time sheets, and inventory. This data is predictable, orderly, and familiar to businesses. This type of data stands in contrast to big data. Where the former data was structured in rows and columns, the latter data is not structured and may seem overwhelming to work with due to the volume, variety, and data type. The emergence of big data has changed the management accountant's task. A business utilizing big data would have invested significant resources to collect, process, prepare, and eventually analyze it and consequently expects deeper insights and knowledge as result. Essential for any type of data, beyond being big or not, is that it be of high quality (Chae et al., 2014). High-quality data is complete, precise, valid, accurate, relevant, consistent, and timely (Redman, 2013). Research shows that high-quality data is an important business resource and asset (Chae and Olson, 2013; Redman, 1996) and has a tremendous impact on an entity's performance (Forslund and Jonsson, 2007; Gorla et al., 2010). Poor quality data of any type and from any source can negatively impact the management accountant's work, rendering forecasts to be in e
or. Valuable analysis and forecasts are a result of the most appropriate analytical approach(es) applied to high-quality data (Redman, 1998). Or, as stated by Davenport et al. (2010, pg 23): “You can't be analytical without data, and you can't be good at analytics without really good data.”
2.1.3 Experimental research in management accounting
Experimental research in management accounting has spread in the last decades for at least three reasons. First, regarding content, it allows us to investigate how far the behaviour of individuals and groups within organizations deviates from the predictions of economic theory. Second, regarding methodology, it allows for a stronger causality link than other methods. Third, due to the perfect observability especially in lab experiments, results can be tied closely to underlying psychological constructs and thus establish strong causal links not only between manipulation and results but also the psychological mechanisms in-between.
2.1.4 Managerial Accounting Data Analytics (MADA) framework
Figure 1 The Managerial Accounting Data Analytics (MADA) framework, motivated by Cokins, 2013, pg. 27.
Fig. 1 exhibits the framework for implementing data analytics in managerial accounting based on balanced scorecard theory. According to Cokins (2013), management accounting can be classified into cost accounting, cost reporting and analysis, and decision support with cost planning. Thus, in this framework, management accounting is classified into cost accounting, performance measurement, and planning and decision-making. In cost accounting, management accountants focus on using internal data to generate financial reports of the organization. Performance measurement focuses on the insights, inferences, and analysis of the processes or events that have taken place to measure corporate performance. Data used in performance measurement includes mostly internal data. However, external data, such as industry benchmark information, can be used for performance evaluation. Planning and decision-making involve using the result of both cost accounting and performance measurement to provide accurate, timely, and relevant information in combination with other external information to assist management. External data are heavily used in combination with internal data to provide relevant information for decision-making. Data analytics can be implemented to assist management accountants in all three aspects of management accounting.
For financial reporting purposes, the most applicable type of data analytics is descriptive analytics which helps to summarize and describe the financial situation of a business. In the field of performance measurement, management accountants can utilize predictive analytics, which can employ machine learning algorithms with inputs from descriptive analytics, to provide a prediction of future organizational performance. With the results from both cost accounting and performance measurement, prescriptive analytics are incorporated into planning and decision-making to provide information regarding the optimized solution for decision-makers. Serving as the data source of data analytics, big data is comprised of both internal and external data. Internal data represents data gathered inside the entity (i.e., the company's database). This type of data is generally structured and familiar to management accountants.
On the other hand, external data represents data collected from sources outside the company, such as news, social media, or the Internet of Things (IoT). Usually, external data are unstructured data that can only provide information after being processed by analytics tools. Data types listed in both internal and external boxes represent only examples, not the inclusive list of the entire internal and external data types. In this framework, the BSC methodology is implemented under performance measurement and planning and decision-making aspects of management accounting to incorporate data analytics in the related process. For each perspective of the BSC (financial, customer, internal process, and learning and growth), different types of data analytics are applied to provide a comprehensive measurement of each perspective.
2.1.5 Financial perspective
The ultimate goal of profit-seeking corporations is to increase shareholder value. Kaplan and Norton (2001) point out that companies increase economic value through revenue growth and productivity. Revenue growth generally includes two components: new initiatives (new markets, new products, and new customers); and increase sales of products or services to existing customers by deepening the relationship with them. The financial perspective of BSC measures the financial situation of a company. Cash flow, sales growth rate, market shares, or return on equity (ROE) are examples of measures that reflect the financial perspective of the company (Kaplan and Norton, 1992). Descriptive data analytics provides management accountants with an overall view of the cu
ent financial performance of the company. For example, a ratio analysis that compares ROE and returns on investment (ROI) with historical data gives management accountants information on the growth of the company. On the other hand, comparing such ratios with industry benchmark data describes whether the company maintains a competitive advantage. Interactive visualization tools allow managerial accountants to present financial information much more effectively. Predictive analytics use accumulated historical data to estimate possible future events.
From the financial perspective, predictive analytics are commonly applied for predicting future financial performance. The algorithms for the prediction can be classified as either supervised or unsupervised. Examples of supervised algorithms include support vector machines (SVM), artificial neural networks (ANN), genetic algorithms, bagging and boosting models, C4.5 statistical classifiers and Bayesian Belief Networks (BBN).2 Such supervised algorithms develop the model based on datasets with output. In contrast, unsupervised algorithms do not require datasets with output. Specifically, they classify or cluster the data into different classes, and thus reveal the potential relationships between the data. In general, unsupervised learning is not appropriate for financial predictive analysis because most of the predictions are based on historical value. Other statistics, such as structural models or analytical hierarchy processes (AHP) (Hogan, 2000), are also available as business analytics techniques for management accountants to provide an estimation of the future financial performance of a company.
With the results of descriptive and predictive analytics, management accountants can utilize prescriptive business analytics to recommend the optimal solutions and their likely outcomes. While prescriptive analytics share similar techniques and algorithms as predictive analytics, prescriptive analytics essentially compare the result of such algorithms and aim to find the optimized solution. For example, to reduce cost and at the same time maintain the product quality in a reasonable area for generating revenue, manufacturing companies face the challenge of selecting raw material vendors with a reasonable price and appropriate quality. Incorporating the results generated from analyzing internal data together with data from vendors using SVM, ANN, or C4.5 classifiers, prescriptive analytics help management accountants choose the vendor that will help the company to reduce costs and increase revenue. For example, data from news articles and social media can also be used in the selection of a vendor. Besides cost reduction, with prescriptive analytics management accountants are also able to provide valuable information on other issues from the financial perspective, such as exploring new markets, new products, and new customers.
3 Chapter 3
3.1 Research Methodology
The methodological approach employed to answer the central question of this research by gathering and analyzing data and drawing a conclusion is elaborated. In more detail, the methods of data collection, the selection of the sample, the research process and the type of data analysis employed are outlined. Moreover, the choice of the methodology employed is justified.
3.1.1 Research Design
Research methodology is a process used to collect information and data for further steps of analysis and conclusion. In effect, when a researcher discusses research methods, he is not only discussing the research methods but also deliberates on the logic behind the selected methods in the context of this research. According to Kothari (2010), a researcher should explain why he or she is using a method or technique and why he or she is not using others so that research results can be evaluated either by the researcher himself or by others (Kothari, 2004).
This research employs semi-structured interviews. This approach enabled the researcher to gain an in-depth understanding and experiences of knowledgeable individuals expertly involved in the Big Data area. Three individuals from three different...