Importance Of Big Data In Accounting

Importance of Big Data in accounting

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As discussed by Gandomi and Haider (2015) Big data is considered as the data sets which are so “voluminous and complex” that the different type of the traditional system of the data collection techniques are not appropriate to deal with them. The main challenges pertaining to the big data includes “capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source”. The various number of the ideas associated to the big data are originally based on the three concepts namely “volume, variety, velocity”. The latter attributed concepts are further seen with veracity or the noise is in the data and value.

The terminology of the big data is applied to the “predictive analytics, user behaviour analytics” and certain advanced method by extracting the value from the data. This has been further seen to be applicable to the various type the concepts which are related to the “spot business trends, prevent diseases and combat crime”. The various types of the scientists, practitioners of medicine and business executive and government alike are able to regularly meet with the difficulties pertaining to the “large data-sets in areas including Internet search, fintech, urban informatics, and business informatics” (Tsai et al. 2015).

The main scope of the study relates to address the importance of big data in accounting. Some of the other relevant discussions of the study has been seen to include the general complexities presented by Big Data and the various types of the additional complexities for management accounting information. The latter part of the discussing further seen to be conducive in presenting the information associated to how the accountants leverage big data, to enable better business decision making. The main form of the discussions has been seen to be based on the “future opportunities, implications and challenges for management accountants” to upsurge use of big data in their decision making and the modification to these new environments by management accountants (Goes 2014).

As stated by George, Haas and Pentland (2014), Big Data or the data analytics has been creating a buzz in the business sector which varies “across numerous spheres”. As per the “Association of Chartered Certified Accountants (ACCA)” the big data relates to the chunk of data which are gathered progressively and combined with using tools and “technologies” such as “debit cards, the Internet, social media, and electronic tags”. A massive amount of the data unstructured or does not conform to an “explicit and predefined data model”. The data sharing will allow for both internal and external movement of data which can be improved by the accounting professionals. This will considerable save time and money and able to escalate efficiency (Zhou, Fu and Yang 2016).

General complexities presented by Big Data and additional complexities for the for-management accounting information

The role of Big Data in accounting has been seen to be based on the various types of the concepts which are associated to the new types of the data being accessible. The different types of the “video, audio, and textual information made available via Big Data” will be conducive in providing improved “managerial accounting, financial accounting, and financial reporting practices”. In terms of the managerial accounting information the use of “Big Data” has been seen to be important for contributing to the development and “evolution of effective management control systems and budgeting processes”. In terms of the various types of the financial accounting concepts the contribution of the big data has been observed with the potential to enhance the  quality and relevance of the accounting information which has been seen to be related to the enhancing the “transparency and stakeholder decision making”.in terms of the general reporting the Big Data is observed to the “useful in providing useful information” as the “dynamic, real-time, global economy evolves” (Costa 2014).

The huge opportunity in the accountancy is also observed in using the big data for real time financial predictions. The incorporation of the information pertaining to the big data by the accountants has been further based on the use of the data in terms of measuring the financial performance and how they regularly serve to the operations of the business. Some of the main form of the depictions of the information has been further seen to be identified with the use of the big data in terms of getting access to the unprecedented amount of the information which has been seen to be conducive for financial advantage. The use has been further seenw3ith the major significance which are seen to be considered for “increasing operating efficiencies, assess risks and identify advantages and weaknesses through analysis” (Mackie, Sim and Johnman 2015).     

It has been further discerned that the use of the big data has been able to provide the accountants with the necessary information associated to the positioning of the accountants in terms of the positioning as partners to the business instead of their more traditional accounting role. It has been also discerned that the finance department are seen to be “implementing predictive analytics tools together with customer data to make forecasts”. The nature of the services that the “accounting professionals” provide and the Liason may be varying significantly due to the “advancement self-service data recovery”. In addition to this, the role of the accountants has been able to assume that the services will not be limited to the reporting of the financial data. The use of the varying nature of the data sets the accountants will be able to identify varicose types of the required alternatives which may be used by the decision makers (Bughin 2016).

The application of the Big Data for complex operations has been the main form of the hurdles which has originated from the “high-dimensional data, i.e. data sets involving many parameters, as well as a large number of observations covering a wide range of combinations of these parameters”. In general, the information contained in the big data are seen to be having no specific hypothesis. The second most crucial aspect of the concern has been discerned with the characteristic concerns the “automation of the entire scientific process, from data capture to processing to modelling” (Chen, Mao and Liu 2014).

As more and more new data is becoming “accessible, big data has the possibility of deprecating rapidly”. This is seen to be more evident depending on the usage. The use of self service and automation has eroded thee demand of the product to a large extent.  It has been further seen that the use of the varied scope of the information has been seen to be more susceptible to erode “the demand for definitive internal reporting” and cultural impediments may disrupt the internal reporting of the cultural impediments which may significantly affect the internal distribution of the data.  The internal auditing PICPA has suggested that the data analytics has been able to state on the decreasing cost/benefit ratio due to the increased cost of implementation of the big data (Crawford, Miltner and Gray 2014).

The main concerns for the additional complexities pertaining to the management accounting information is seen with “Heterogeneity and Incompleteness”. In situations when the accountants feed any information, great deal of the heterogeneity is seen to be comfortable tolerated. The various nuances of the information have been seen as the contributing factor for the “analysis algorithms expect homogeneous data and cannot understand nuance”. For instance, in a chartered accounting firm, the clients having multiple queries needs to be dealt with multiple procedures. It needs to be further discerned that the use of the various types of the information has been further seen to be based on the restriction in the structural flexibility. The availability of the wide range of the data set will make it considerably difficult task for the manager to trace the exact data set which will be required to solve the queries of the clients (Wu et al. 2014).

The scale of the data has been considered as another important consideration which has been related to managing growing volumes of data which has been a “challenging issue for many decades”. Several challenges have been further identified with the “parallel data processing” techniques that were applied in the “past for processing data across nodes don’t directly apply for intra-node parallelism”. The dramatic shift of the information has been identified with the moving towards “cloud computing, which now aggregates multiple disparate workloads with varying performance goals”. It has been also discerned that for many years “hard disk drives (HDDs) were used to store persistent data”. However, in the recent times the HDDs has been depicted with “slower random IO” and sequential “IO performance”. The replacement of the HDDs with the SSD are seen to be becoming a major concern in the organizations which are dealing with the Big Data. The newer storage does not have the same large spread in performance among the “sequential and random I/O performance”, this takes into consideration the which are designed with storage “subsystems for data processing systems”.  The main inferences of this changing storage is considered as “subsystem potentially” depicted with the data  processing, including  query “processing algorithms, query scheduling, database design, concurrency control methods and recovery methods” (Demchenko, De Laat and Membrey 2014).

It has been further seen that Big Data is threatened by the need for data privacy. Several surveys have been able to suggest that data privacy is greatest concern for the users of data analytics. Some of the most evident nightmares of big data for the market researchers is discerned with sampling error and sampling bias. It has been further observed that Big Data and the inevitable challenges and obstacles lies in the way of its utilization (Levin, Salek and Steinbeck 2016).

In general, it has been assessed that the “management accountants” are positioned to play an important role in the application and implementation of the business analytics in their organizations and move beyond the traditional techniques. This is seen as a move to adopt the “transaction-based accounting to analytics”. This form of the emerging technique has been conducive for the management accounts in appropriately interpreting the data. As per the recent development in technology the business analytics are seen with the need of equipping new tools and process to build the value in the organization. The sources of the data have been identified with the internal data sources identified with the spreadsheet files, CSV files and Microsoft Access Files. The main considerations of the use of the internal data source has been further observed with the SQL queries ERP Data warehouse. The management of the external sources has been further identified with the use of google analytics, SEC XBRL database, ZenDesk and Salesforce (Fosso Wamba et al. 2015).

The opportunities of the big data analytics for the management accounts has been considered in five main areas. These are considered with “five main areas that provide a clearer picture of business analytics applications”: “(1) franchise sales analysis, (2) accounts receivable and credit analysis, (3) accounts payable analysis and payment monitoring, (4) mergers and acquisitions (M&A) due diligence, and (5) forensic accounting”. The franchise sales analysis is considered with the analysis of he sales metrices and determination of the point of sales promotion. The future opportunities for the accounts receivable by the business analytics is seen with the tracking of “days’ sales outstanding (DSO)”. Business analytics has several opportunities in “fraud detection analytics (FDA)” for identification of any instance of “fraud, bribery, and corruption in companies”. It has been further identified that the “Improper Payments Elimination and Recovery Improvement Act of 2012 (IPERIA) mandates that U.S. federal government agencies address improper payments and requires the agencies to implement internal controls to identify fraudulent activity” (Hu et al. 2014).

The management accountants are seen to be having a difficult task in case they fail to “leverage the opportunities” provided as per the digital information revolution. This may significantly jeopardize the operating performance of the organization and affect the competitive advantage. To efficiently handle the future challenges, the management accountants need to be particularly aware of the techniques which are associated to the identification, analysis and use of the data. This does not imply that the management accountants adopt the complex analysis. This further implies that the companies need to develop a specific strategy while integrating the “business analytics into its corporate information management strategy”. The implementation process of the business analytics process needs to be defined with the “business analytics objectives”, defining the organizational structure, creating cross functional teams and preparation of the business analytics framework and plan. Some of the other implementation process has been followed with the business analytics software and training. The next adjustment to these new environments by management accountants needs to be considered with the implementation of a business analytics system, evaluate and revising the system. Some of the various types of the other challenges are identified with the “awareness, interoperability, security and analysis quality” (Hu et al. 2014).

The awareness of the management account may not “understand how accessible and valuable business analytics is to their companies”. There has been significant nature of the considerations made for the “Big Data” and “business intelligence (BI)” that is outside the scope of small companies because of their “lack of technical knowledge, inadequate IT infrastructure, and cost constraints”. Despite of the identified limitations the “management accountants” need to think in terms of the business analytics implementation which has been considered with the “readily available, affordable, and easy to use” systems. Some of the various types of the challenges associated to the interoperability is identified with linking the structured and unstructured data. It has been also discerned that much of the available data is unstructured and henceforth inharmonious with the organisation’s data (Kambatla et al. 2014).

Conclusion

The importance of Big Data in accounting has been traced with the various types of the concepts which are associated to the new types of the data being accessible. The different types of the “video, audio, and textual information made available via Big Data” will be conducive in providing improved “managerial accounting, financial accounting, and financial reporting practices”. It has been further discerned that the use of the big data has been able to provide the accountants with the necessary information associated to the positioning of the accountants in terms of the positioning as partners to the business instead of their more traditional accounting role. It has been also discerned that the finance department are seen to be “implementing predictive analytics tools together with customer data to make forecasts”. General complexities presented by Big Data and additional complexities for the for-management accounting information is depicted with big data possibility of deprecating rapidly. This is seen to be more evident depending on the usage. The use of self service and automation has eroded thee demand of the product to a large extent.  It has been further seen that the use of the varied scope of the information has been seen to be more susceptible to “erode the demand for definitive internal reporting and cultural impediments” may interrupt the internal reporting of the cultural impediments which may significantly affect the internal distribution of the data. It has been further identified the management accountants leverage big data with the application of “(1) franchise sales analysis, (2) accounts receivable and credit analysis, (3) accounts payable analysis and payment monitoring, (4) mergers and acquisitions (M&A) due diligence, and (5) forensic accounting”. The franchise sales analysis is considered with the analysis of the sales metrices and determination of the point of sales promotion. 

References

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