INFS 5023-Information Systems for Business Case Study Sample
TASK DESCRIPTION
Topic:
In today's technologically advanced world, the fields of IT (Information Technology), Information Systems, and Accounting have become increasingly interdependent, driving significant changes and innovations across various industries. Explore and analyse the pivotal roles these professions play in modern society, their interconnectedness, and how they collectively contribute to enhancing organisational efficiency, decision-making, and overall business success. Discover how the emerging technologies are bringing significant impact both positively and negatively to the accounting field.
Follow the steps below:
• Choose an accounting professional from your network.
• Explore the possibility of interviewing that person for about 20-25 minutes. Then, fix an appointment for the interview and video record the Interview via zoom or any similar tool.
• Please assure them that this is only for assignment purposes and will not be published publicly Anywhere.
• Include the interviewee's -LinkedIn profile/email address for authenticity in the report.
• Prepare the interview questions to elicit information for the topic mentioned above. Design you questions carefully to get enough information and avoid yes/no answer questions. Fram your questions with phrases like 'Please explain/describe..etc.
• Use any software/ platform that allows doing the audio and video recording.
• Save the recording.
• Upload to youtube and include the link at the end of the report.
• When you upload, there are three privacy settings-Public, Unlisted and Private. Please choose the Unlisted option.
• Analyse the findings of the Interview and write a report.
Solution
Introduction
Big data analytics technology refers to an advanced technology that analyses large and diverse data and turns the data into a structured representation. Big Data analytics has become highly important these days in terms of adopting business decisions in the accounting process. The analytics identifies the latest market trends, consumer preferences, and emerging opportunities in the target market. For Assignment Help, Providing real-time access to data, applying Data analytics to big data, evaluating Data analytics and audit, ensuring better accounting risk management, predicting real-time market trends, selecting agile pricing models, streamlining sales and sales funnel optimization, and understanding optimal customer profiles are some of the applications of Big Data analytics on business accounting process which will be addressed in the study. The study will also highlight methods, theories, challenges, and recommendations for the use of Big Data analytics in the accounting process of a business decision. The application of big data analytics in accounting decisions in business can find out solutions to the accounting problem in the business.
Main Body
Main findings
An application of Big Data analytics is highly effective in terms of analysing and evaluating large-size data. Balios et al. (2020:211-219) have considered Big Data analytics to be a significant tool that not only assesses the business based on the current accounting situation but also addresses the factors that can lead to accounting issues during decision-making. Accounting professionals in business organisations use Big Data analytics. An analysis of the applications of Big Data analytics in the decision-making at different business organisations about the accounting process can be effective in understanding the multiple benefits of the technology in the accounting process.
Real-time access to data
Big Data analytics is a significant tool that allows the accountants of business organisations to access real-time data. Huttunen et al. (2019:16-30) have pointed out Big Data analytics to be a tool for accessing large amounts of accounting data in a limited time. It is helpful to instantly rectify the errors in the accounting reports and escalate the efficiency level of accounting decisions in business. For example, auditing errors, numerical errors, and statistical errors in the accounting system are identified using Big Data analytics. The identification of errors allows better business decision-making. Moreover, it is also effective in setting performance benchmarks in the accounting standard.
Application on big data
Data analytics works the best in sorting and arranging big data. Omitogun and Ap-Adeem (2019:92-113) have stated the effectiveness of using Data analytics for analysing big data in accounting standards while making business decisions. A perfect application of Big Data analytics helps sort and arrange big data in accounting that is effective in taking significant business insights to credit future possibilities and address financial tasks. Stacheva-Todorova (2018:126-141) has supported the contribution of Data analytics in evaluating large-size data in terms of highlighting the effectiveness of artificial intelligence on Data analytics for analysing big data. The author has further commented that artificial intelligence is sometimes used along with Data analytics to automate the data analytic process so that large-size data can be analysed in the minimum time and a minimum effort.
Evaluation of Data analytics and audit
Data analytics is highly effective in improving auditing quality. Sun and Vasarhelyi (2018) have embraced the importance of sexual Data analytics on the grounds of auditing by addressing it to be a tool to improve audit quality by providing clarification of income statements and balance sheets. The use of Big Data analytics for auditing can be separated into three parts; planning, field work, and reporting. The planning phase includes risk assessment and profiling, data simulation, and statistical data sampling. The fieldwork stage includes constant auditing and monitoring, fraud indication and detection, and predictive risk identification. Finally, reporting standards include risk quantification real-time exception management, and root cause investigation. Rozario and Vasarhelyi (2018) supported the concept stating that the use of smart contracts of Data analytics are highly effective in improving auditing standard. The tools that big data analytics uses to improve auditing quality are Hadoop, MongoDB, Talend, Cassandra, Power BI, Excel, and artificial intelligence.
Accounting risk management
Data analytics is excellently used for accounting risk management. Kamau (2022:1-8) has pointed out the effectiveness of using Data analytics for maintaining transparency in auditing procedures to reduce accounting factors. The accounting risk factors that most companies face are income risk, expenditure risk, assets and liabilities risk, and credit risk. The Big Data analytics tools that are used to reduce risk factors from the accounting standard include Apache Spark, RapidMiner, Tableau, Knime, Apache Hive, etc. All the big data analytic tools are efficient in tracking and predicting risk factors through elements like advanced customer behaviour analysis, economic trend analysis, and transaction pattern analysis. Data analytics analyses and hence points out the accounting risk factors so that the factors can be eliminated before adopting any conclusive business decision. For example, economic trend analysis is conducted using Tableau or Apache Hive to predict pricing Trends and customer behaviour Trends before adopting any product or pricing strategy. Kaya et al. (2019:82) have supported the use of various data analytic tools to track and manage which factors associated with the accounting process in business. The authors have further commented that Big Data analytics, data mining, and different emerging disruptive Technology can also be used to predict both the internal and external risks of the business organisation. The prediction of internal and external risk factors supports making conclusive decisions on business activities.
Prediction of real-time market trend
The prediction of real-time market trends helps in formulating accounting standards for business decisions. According to Desai and Vidyapeeth (2019:196-200), the prediction is highly effective in terms of pointing out the trends and the activities that take place in the market. The best use of Data analytics is highly effective in addressing the latest trends in the market. A perfect identification of the latest market Trends is always beneficial to process and analyse the real-time information of what the customers and demanding and how the demand can be satisfied. The demand of customers sets the way that the accounting should process its budgeting procedure. As a result, the companies get a custom to what the market rest factors are and what the expected market print can be so that decisions can be made based on the situations. For example, Amazon company uses its big data analytic tools to access customer trends and market risk factors. The company also uses Big Data analytics to estimate the level of self-service that customers require (amazon.com, 2019). Outcomes of using Big Data analytics to get real-time insights include responding quickly to the market Trend, estimating customer demands effectively, and accessing minute details about the target market to optimise accounting and business risk.
Selection of agile pricing model
The pricing model selection is one of the most challenging tasks for the accounting segment of different business organisations. An effective use of big data analytics is one of the most convenient ways to select the appropriate pricing model by adjusting to the changing market situations (Forbes.com, 2022). Most of the accounting segments of different companies rely too much on big data analytical patterns in making necessary changes in pricing arrangements keeping alignment with the changing market situation. For example, Spotify company selects its pricing pattern by making necessary adjustments with the help of Big Data analytics to keep different pricing segments for developing markets and developed markets. The outcome of using Big Data analytics for selecting an agile pricing model is the clarification of pricing standards and identification of expected pricing trends in future.
Streamline sales and sales funnel optimization
Data analytics make necessary changes in optimising the sales process that can be improved. The optimisation in the sales process keeps a perfect balance between accounting cash flow in the organisation and sales revenue return (Forbes.com, 2022). Moreover, data analytics enables the power to optimise the sales funnel to adapt appropriate business decisions. For example, Big Data analytics use artificial intelligence to create their own data by evaluating the market Trends. As a result, the sales funnel of the organization gets automatically optimised based on the pattern of data that has been evaluated by the analytics.
Optimal customer profile understanding
The optimization of customer data is an advantage of using Data analytics. Big Data analytics provides an overall view of customer behaviour and preferences from multiple data sources (indiatimes.com, 2023). Examples of data sources are online interactions, social media sentiment, customer purchasing history, and the demographic information of the customers. Some of the major outcomes of understanding customer profiles include better budget setting for each customer segment, better self-service reporting, better ROI prediction, and better up-to-date information about customers. The information is helpful for business organisations to adapt significant accounting decisions (Forbes.com, 2023).
Interpretations
Methods of Big Data Analytics
Descriptive analytics
Descriptive analytics is a significant data analytic method for evaluating accounting processes to track the ongoing accounting operations in an organisation. Mikalef et al. (2019) address descriptive analytics to be a significant tool for accountants to answer "what happening" questions. It is effective in business decision-making because accountants use it to create reports and financial statements.
Diagnostic analytics
Gymnastic analytics is a significant data analytic tool for the accountant to respond to “why” questions in accounting operations. Jopri et al. (2018:5399-5408) have stated Diagnostic analytics is a tool to create dashboards for evaluating current accounting information and historical data to predict the expected outcomes.
Predictive analytics
Predictive analytics is used by accountants to create business forecasts with the best use of big data. Smys (2019:77-86) has addressed predictive analytics to be a business analytic to address potential business outcomes.
Prescriptive analytics
Prescriptive analystics is used by accountants to produce a fact-driven report to understand where the business should go and what can be the actionable steps. Wang et al (2022:3586-3594) have stated it to be a significant analytic method to deliver data-driven business decisions.
Theoretical implementation
EDA theory
EDA theory is also termed as Exploratory Data Analysis. Mittal (2020:4) has stated the theory to be significant in terms of establishing key variables, addressing data errors, checking assumptions, and estimating parameters. Accountants in business organisations can use it to respond to “what next” questions by developing appropriate accounting strategies.
CDA theory
CDA theory is also referred to as Confirmatory Data Analysis theory. Duran et al. (2021:262-282) have stated that the theory is effective in terms of using Data analytics to discover a chain of phases of trials to address new hypotheses and information. Traditional statistical tools like regression analysis, variation analysis, testing hypotheses, and developing estimates are some of the tools for performing CDA.
Challenges
The challenges are as follows:
• Time-consuming: The use of data analytic Technology for accounting is time-consuming.
• Privacy concerns: Data privacy and security concerns a major challenges of using Data Analytics Technology. Moreover, it also impacts on data quality and Data integrity.
• Requirement of skilled staff: Any compromise with the data analytical skill would result in inappropriate outcomes for business decisions.
Conclusion
An evaluation of Big Data analytics Technology for effective business decisions specialising in the accounting process has been addressed. Providing real-time access to data, applying Data analytics to big data, evaluating Data analytics and audit, ensuring better accounting risk management, predicting real-time market trends, selecting agile pricing models, streamlining sales and sales funnel optimisation, and understanding optimal customer profiles have been found as some of the significant applications of big data analytic Technology to improve accounting standard in business decision-making. Descriptive analytics, Diagnostic analytics, predictive analytics, and prescriptive analytics have been addressed as methods of Big Data analytics specialising in accounting standards. Moreover, EDA theory and PDA theory have been mentioned while addressing relevant theories for the application of Big Data analytics. Time challenge, privacy challenge, and skilled staffing challenge have been addressing three different challenges of using big data analytic Technology.
Recommendations
The following are the recommended actions to mitigate the challenges:
Focusing on long-term goals: The accounts management of business organisations must focus on long-term goals if they plan to use Big Data analytics. It provides the best result in the long term.
Endpoint filtering and validation: A mobile device management solution can be used to ensure in-point filtering using trusted credentials and perform research verification to ensure safety concerns. Moreover, statistical similarity detection software can also be used to protect data.
Hiring skilled staff: Highly skilled staff should be hired to manage and control Big Data analytics in accounting functions at business organisations.
Reference List
amazon.com (2019) Big data, Amazon. Available at: https://aws.amazon.com/big-data/what-is-big-data/ (Accessed: 31 August 2023).
Balios, D., Kotsilaras, P., Eriotis, N. and Vasiliou, D., 2020. Big data, data analytics and external auditing. Journal of Modern Accounting and Auditing, 16(5), pp.211-219. https://www.academia.edu/download/88953656/5ed99f943e596.pdf
Desai, V. and Vidyapeeth, B., 2019. Digital marketing: A review. International Journal of Trend in Scientific Research and Development, 5(5), pp.196-200. https://www.academia.edu/download/90185943/ijtsrd23100.pdf
Duran, V., Topal, S. and Smarandache, F., 2021. An application of neutrosophic logic in the confirmatory data analysis of the satisfaction with life scale. Journal of fuzzy extension and applications, 2(3), pp.262-282. http://www.journal-fea.com/article_131883_6fe96ff2f8bfb13ca7edd45f0f1831a4.pdf
forbes.com (2022) Council post: Using big data and data analytics for Better Business Decisions, Forbes. Available at: https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2022/08/29/using-big-data-and-data-analytics-for-better-business-decisions/?sh=11876ad42777 (Accessed: 31 August 2023).
Huttunen, J.E.N.N.I.F.E.R., Jauhiainen, J.A.A.N.A., Lehti, L.A.U.R.A., Nylund, A.N.N.I.N.A., Martikainen, M.I.N.N.A. and Lehner, O.M., 2019. Big data, cloud computing and data science applications in finance and accounting. ACRN Journal of Finance and Risk Perspectives, 8, pp.16-30. http://www.acrn-journals.eu/resources/SI08_2019b.pdf
Indiatimes.com (2023) The role of Big Data Analytics in business decision making, Times of India Blog. Available at: https://timesofindia.indiatimes.com/blogs/voices/the-role-of-big-data-analytics-in-business-decision-making/ (Accessed: 31 August 2023).
Jopri, M.H., Abdullah, A.R., Sutikno, T., Manap, M., Ghani, M.R.A. and Hussin, A.S., 2018. A diagnostic analytics of harmonic source signature recognition by using periodogram. International Journal of Electrical and Computer Engineering, 8(6), pp.5399-5408. https://www.academia.edu/download/64005996/67%2014Jul18%2014317%20(Edit%20I).pdf
Kaya, C.T., Türky?lmaz, M. and Birol, B., 2019. Impact of RPA technologies on accounting systems. Muhasebe ve Finansman Dergisi, (82). https://www.researchgate.net/profile/Burcu-Birol/publication/332123847_RPA_Teknolojilerinin_Muhasebe_Sistemleri_Uzerindeki_Etkisi/links/60153d7e92851c2d4d0337d3/RPA-Teknolojilerinin-Muhasebe-Sistemleri-Uezerindeki-Etkisi.pdf
Mikalef, P., Pappas, I., Krogstie, J. and Pavlou, P., 2019. Big data and business analytics: A research agenda for realizing business value. https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/2644384/Mikalef.pdf?sequence=4
Mittal, S., 2020. An exploratory data analysis of COVID-19 in India. International Journal of Engineering and Technical Research, 9(4). https://www.researchgate.net/profile/Sarvam-Mittal/publication/340965511_An_Exploratory_Data_Analysis_of_COVID-19_in_India/links/5ea7d74845851553fab5f52d/An-Exploratory-Data-Analysis-of-COVID-19-in-India.pdf
Omitogun, A. and Al-Adeem, K., 2019. Auditors’ perceptions of and competencies in big data and data analytics: an empirical investigation. International Journal of Computer Auditing, 1(1), pp.92-113. https://www.iacae.org/english/Publication/Abstract/IJCA2019.pdf#page=97
Rozario, A.M. and Vasarhelyi, M.A., 2018. Auditing with Smart Contracts. International Journal of Digital Accounting Research, 18. http://www.uhu.es/ijdar/10.4192/1577-8517-v18_1.pdf
Smys, S., 2019. Survey on accuracy of predictive big data analytics in healthcare. Journal of Information Technology, 1(02), pp.77-86. https://scholar.archive.org/work/bsuchvcwhjcj3aojozm4zbyvcq/access/wayback/https://www.irojournals.com/itdw/V1/I2/03.pdf
Sun, T. and Vasarhelyi, M.A., 2018. Embracing textual data analytics in auditing with deep learning. International Journal of Digital Accounting Research, 18. http://www.uhu.es/ijdar/10.4192/1577-8517-v18_3.pdf
Wang, S., Tian, X., Yan, R. and Liu, Y., 2022. A deficiency of prescriptive analytics—No perfect predicted value or predicted distribution exists. Electron. Res. Arch, 30, pp.3586-3594. https://www.aimspress.com/aimspress-data/era/2022/10/PDF/era-30-10-183.pdf
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