Essay Sample on How Data Clustering Affects Accounting

Introduction

According to Zerhari, Ayoub, and Salma (2), big data is defined with three elements called the 3Vs, and they include Variety, Volume, and Velocity. It refers to the data which are too complex, large, and dynamic. In this area, data are usually challenging to manage, capture, analyze, and store using traditional management tools. Thus, this issue causes a new challenge referred to as data clustering. Clustering is a valuable data tool and mining used in analyzing big data. However, some challenges are encountered in the application of clustering techniques to the big data due to the new challenges which are caused by big data. This issue has affected the accounting sector in different ways.

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Big data entails extensive chunk of data which is aggregated and gathered steadily using technologies and tools like social media, internet, electronic tags, and debit cards. Big data and data clustering have several impacts on accounting. For instance, big data can support the companies to appraise their data assets through expanding dynamic assessment techniques. In conducting this process, the finance professionals and accountants should determine valuable data and even create valuation technique which can be utilized in identifying the key suppositions (Zerhari, Ayoub, and Salma 3). There is also furthering of data using control and stewardship. The idea is because accountants and comparable professionals might assist in the turning of internal data sets to more essential, in-demand, and vigorous.

The use of big data in companies can also lead to more explicit support in real time (Vysochan 16). The kind of services which are offered with the accounting professionals and their liaison with decision makers in the corporate world which varies with the advancement of the self-service data delivery. The roles and responsibilities that are conducted with the accountants cannot be judged with the limited reporting of the financial data. Through the evaluation of different datasets in which it is easy to determine alternative which can be utilized with the decision makers. Managers and the accounting professional can make decisions easily as they get support in real time.

Data sharing of big data can also lead to created values. Both accounting professionals may better internal and external movement of information and data. This issue will assist in saving time and money and will escalate efficiency (Vysochan 17). Unfortunately, there are challenges that people can face when using big data. One of the problems is because more now data is turning to be accessible, then big data can rapidly depreciate. This is because data value usually diverges depending on the way it is used. Furthermore, automation and self-service can easily erode the need for definitive internal reporting, and cultural impediments can also disrupt the distribution of internal data. As for the issue of internal auditing, PICPA reported the issue of the data analytics transforming the program of the audit through instituting to the companies who need ways to better cost-benefit ratio regarding their internal function and the idea of continuous auditing (Zerhari, Ayoub, and Salma 3). It entails the compilation of gauges and evidence with an internal auditor concerning the processes, transactions, controls, and system of information technology and the integration of a continual risk assessment process.

Data clustering is essential for big data in accounting because it can help in fraud detection. Most of the studies on accounting literature in the past have mainly focused on the management of fraud using the best prediction models in logic regression. Data clustering is essential in the process of detecting fraud in accounting because it groups the big data into smaller groups which ease analysis for most of the accountants and financial professionals. Moreover, the greater the similarities which exist in a group, the higher the differences in the groups and this makes it easier for a company to get the best clustering results. The subgroups are mainly differentiated with the selected variables. Through these variables, it is easy to identify different data sets for several accounting activities in a firm.

Data clustering on big data helps in understanding the structure of data and data exploration in accounting (Thiprungsri 1). Data clustering assists in finding the similarities which exist between observations and then grouping them. The main steps in cluster analysis include selecting similarities and dissimilarities measures and the second one is selecting the procedures which can be utilized in choosing the cluster formations procedures. Additionally, there are different techniques or options which are available for the two steps which makes cluster analysis more of an art than science. In accounting, the aim of conducting clustering is to find if a specific group may be partitioned to different groups. Therefore, data clustering for big data is essential in the accounting sector.

Clustering data is mainly used in the accounting sector and other areas such as marketing research and specifically market segmentation. Several marketing strategies can be created and applied on every segment of the customers and clusters. Segmentation of the market using data clustering have been conducted in various industries which includes banking and finance. Although data clustering is essential in market segments, it also comes down to the accounting section since markets need to balance their finances using the goods purchased and the profit made by the sellers and grouping segments to reach the targeted customers.

Data clustering for big data affects accounting in that it offers direction of the accounting idea establishment which is addressed to the essential management activities in the distributed network structures. The accounting capabilities have been found to solve most of the challenges of cluster interaction. The main features of cluster accounting include specific accounting objects and considering the industry-specific activities of the cluster members. The specific accounting objects that are used in enhancing data clustering includes innovation expenditure, transaction costs, implementation of marketing activities, government grants, investments, and brands (Thiprungsri 4). Furthermore, data clustering for big data makes it easy for the consideration of the multidirectional accounting data and information requests of the receipts. It is also through this that it creates statistic, internal, and social reporting.

Data clustering impacts accounting by showing the way clustering activity indicators are different from profit. It is through data clustering that an organization can identify the level of its innovation development (Thiprungsri 3). An innovation organization can easily compete with the competitors as it has ideas which might assist in solving the problems that are faced by most consumers in the world. Data clustering helps in the reduction of costs per unit in the production process. Reduction of costs helps a company to make higher profits as it can spend less money and resources on the production process. It can make a firm to reduce the price for its products as it spends less money on production which is a benefit to the customers and will also attract more customers.

Data clustering has also impacted the accounting sector through the creation of new accounting functions (Thiprungsri 1). Their accounting functions are important in every company because it makes it easy for an organization to complete its accounting activities on time. Additionally, through the new functions, the accounting professionals can easily identify the issues that may affect the financial statements of the company due to big data. There is big data in the accounting sector since it involves several activities and that is the reason as to why data clustering is important in this sector.

Conclusion

In conclusion, data clustering for big data is essential in the accounting sector. For instance, through the grouping of data, it is easy for it to identify fraud in accounting tasks and find the best ways of preventing these frauds in future. Also, data clustering plays a significant role in market segmentation which is conducted through finance and banking. The specific accounting objects such as government grants, transaction costs, innovation expenditures, and implementation of the marketing activities have been performed through data clustering of big data in accounting. Data clustering has also impacted accounting through an increased level of innovation and decreasing the production costs which increases the number of customers and profits as goods are sold at affordable prices. Data clustering results in the expansion of accounting tools which is important for the productivity of a company. Therefore, most organizations should use data clustering especially the ones with big data.

Works Cited

Thiprungsri, Sutapat. Cluster analysis for anomaly detection in accounting. Diss. Rutgers University-Graduate School-Newark, 2012.

Vysochan, O. S. "Cluster Accounting Concept: Characteristics and Features." Oblik i Finansi 71 (2016): 15.

Zerhari, Btissam, Ayoub Ait Lahcen, and Salma Mouline. "Big data clustering: Algorithms and challenges." Proc. of Int. Conf. on Big Data, Cloud and Applications (BDCA'15). 2015.