Discuss about the Opportunities And Challenges Of Big Data In Accountancy.
Big data is a term widely used in the business environment in the current years. The term is too broad that the professionals are keeping their minds and professionalism together to come up with a simplified comprehensible definition. Growth of competition in business environment and advanced development of technology have in the current world have made businesses to fight their way out for survival in the market through acquisition of big data (Boons, Frank, and Florian Lüdeke-Freund. 2013). Businesses are nowadays changing from small data to big data to enjoy the benefits it encompasses. So much advantages are enjoyed from incorporation and adoption of big data by big data users Gandomi, Amir, and Murtaza Haider, 2015). Big data is therefore be defined as extremely vast volumes of complicated data that are beyond handling using the traditional tools but rather require sophisticated analytical methods to handle. Structured, semi-structured and unstructured are the three major types of big data. Data bases that would be used to handle big data will depend on the structure and type of the collected data (Wamba et al. 2015). The collected data fit the whole business needs as they are directly from their consumers ranging from their views concerning the supplied products by the business to their test for the products and the business services among other views. Each business department thus get their portion from the leveraged big data including the marketing department, purchase department, sales department, accounting department etc. each department tighten their waste towards meeting the objectives of the business as the requirement of the current market obtained from the leverage of big data. Even though bid data have become so lucrative to businesses and have now been adopted for use by variety of business companies, the idea and the relevance importance is still very new and fresh in the business accountancy. In this essay, we shall discuss some of the opportunities that can be enjoyed and challenges associated to them when using big data in accounts.
Accounting professionals and the business community at large should embrace the opportunities that are accompanied by the adoption of big data and data analytics to help them improve on their efficiency, effectiveness and efficacy in their work (Abbasi, Ahmed, Suprateek, and Roger. 2016). The initially used spreadsheet is now replaced by big data as millions of data are collected in business databases that are too large to be handled by spreadsheets (Boons, Frank, and Florian. 2013). The highest percentage (98%) of stored data currently in businesses are electronic where the percentage was weighed much lower (25%) in the past decade. The percentage have had such rapid increase due to velocity at which big data is collected and stream in the databases. Both structure and unstructured data are collected in the businesses including the scanned documents, emails, voicemails, images and social media as is experience with Walmart’s (Loebbecke, Claudia, and Arnold, 2015). Customer engagement in business can be automatically changed in business by providing forecasting analytics for business decision making and also automating the operation process.
Incorporation and adoption of big data in accounting exist in two different ways i.e. through integration of different data sources into the accounting information systems and through collection and evaluation of designated data that come from different sources particularly those concerning the fair value assets and liabilities resulting to assumptions on fair value estimates. Connections created between videos, texts and audios to the traditional data in the accounts department need to be looked into exhaustively to the bottom bit of them. In that response therefore, the accountants are supposed to hone their data analytic skills so that they can easily deal with vast volumes of data available at their desks that is not to exclude automatically mined data. In the areas where big data have effect on considerable change is fair value accounting where scattered financial data are collected and merged into unit data that can be used in valuation workflows (Najafabadi et al, 2015). That formed the big opportunity that could be grasped by the financial accountants from the big data leveraging.
Big data also provides opportunity to the managerial accountancy by posing challenges and opening their eyes and minds in their accounting management roles (Garonne et al. 2014). The dynamic change in business environment requires dynamic responses from the accounting professionals as brought forth by big data adoption. Due to that therefore, accounting personnel should change from being decision making supporters to the business partners but to give value input to business and extract evidences from big data that could be used in business rather than relying on the views of the management officials. Accountants in the accountancy positions should have the awareness of the cybercrime and that should sound a warning to them not to store too sensitive financial data in the cloud to avoid cyber-attack (Lee, Jay, Behrad, and Hung-An. 2015). Big data can as well improve the performance of the company since the financial and accounting department could compute and compare the means of the collected data to the currently existing financial data mean and tell whether or not there was improvement in the business performance (Warren et al. 2015). Adopting big data and their data analytics techniques can make it easier for the business to control employees’ work telephone calls and emails if comprehensive monitoring and control systems are adopted. Vast volumes of non-financial data such as social media and website are created by businesses of small scale and large scales. In the current world, financial statements and transactions represent businesses traditionally (Soetanto, Danny and Sarah, 2013). Descriptive analytics on big data help to bring numerical meaning from these data. Through such analytical skills employed on big data, most of the questions about the future of the business performance are answered. The question about which part of the business operation sold highest products and how the expenditures of the business is likely to affect the business sales.
Every activity in business and in each department has to be to the competitive standards in the business environment and big data has the significant effect in changing the accounting standards if fully. Users of big data will be given responsibility of demanding for available data and the needs for future accounting will be balanced and have sensitive data protected.
Financial reports in the business are better acquired by the external auditors through the help of big data and analytics where operational business risks obtain more relevant audit. The auditors’ knowledge is improved concerning the transactions and balances that concern the financial statements derived from the audit analytics methods (Cai, Li, and Yangyong. 2015). Additionally, the audit planning is made easy when the audit analytics methods are fully exhausted and put into use. Carrying out such auditing actions will enable the auditors to evaluate and assess the business risks through the analysis of big data to visualize the patterns, correlation, relationships and any other available variations using the created models. The analytical procedures are therefore improved in all the auditing phases as well as improving their quality thus giving new insights to the auditors in regards to the entities and their risks.
Furthermore, big data analytics is as well used in internal auditing in carrying out various tests on the internal control procedures since it ensures sound control environment. Internal auditors are important in their actions of offering strategic advice to the company by providing actionable information from the analysis of big data. Integration of analytics only has value when used by the auditors to customize and modify extent of audit. The case here is different from sampling used earlier where the sample statistic was being used to estimate the population parameter and come up with the real picture in the field. Various risks such as the possible potential business risks that include the payment fraud and payroll frauds.
Unlike small data, big data are collected in large volumes every time everyday. The more data stream in the databases the more they need bigger space for storage. Trillion gigabytes of data are collected every passing second that inundates the accountancy data handlers (Cai, Li, and Yangyong. 2015). In connection to that, since the data are collected for business purposes, they need backup so that incase the original is lost all will not be lost. Being that the volume of the involved data is enormous, the backup space can be a problem (Abbasi, Ahmed, Suprateek Sarker, and Roger. 2016). High analytical skills are key assets in excavating all the information in big data. The question of where and how big data are stored remains to be a stand still nightmare. This question is not limited to any industry but to any company or business that wish to adopt and incorporate big data use. Since a lot of big data are collected in the unstructured form from their various sources i.e. the social media and other sources are all under the No-SQL databases. In response to that, accountants require frameworks and serious structures to draw the sense out of the collected and stored data where after formal information is channeled from data and bring them to the pattern that can be interpreted and understood in the progress of the business and uphold the high standard performance of the accountancy department and business at large.
Once the accountants collect data, the collected data need to be organized, governed and manipulated in a manner that it can be shared. Data cleansing is needed to be done in the process. Treatment of errors in the data needs to be in a manner that the meaning from the data is not lost. Manually handling this kind of data becomes impossible and one of the things one can never think of doing. As result, the process might be so cumbersome to the accountants since data cannot just be used the way they are stored. The process in turn might be time consuming in their organizations in the preparedness for use in bringing insight in accounting world. This is seen as a big challenge for incorporating bid data in business accounting. The accountants are supposed to have high level of skills to use the big data analytic tools in order to draw the intended meanings from the collected big data, otherwise, the collected big data would be rendered useless. Acquisition of enough knowledge of programs such as the apache Hadoop, Microsoft HDInsight, NoSQL etc. Acquiring such kind of skills to handle big data will require the accountants to seek for extra knowledge on big data handling which might be expensive.
Conclusion
In conclusion, big data brought excitement in business due to detailed information it contains. Accountancy being the center of discussion and checking for the usefulness of big data in business environment, a lot had been found to be possible with big data and analytics. Big data are in three different types i.e. structured data, semi-structure data and unstructured data. Most of the data collected electronically through different sources are at most of the time unstructured. In response to that, sophisticated data analytics tools are needed to be employed for full leverage of big data. It can as well be concluded that big data cannot be handled using traditional tools such as the spreadsheet due to their large volumes. Big data makes performance evaluation of the business simpler and easier as the collected data had relatively high accuracy since they do not just involve samples. Internal auditing have been made easy for the auditors since they are capable of obtaining the big data from the clouds concerning the business thus placing everything to light.
Moreover, the adoption and use of big data in accountancy had been faced with several challenges. Incorporation of big data in accountancy is still young idea though it has been in use. Handling of collected big data, structuring them and carrying out the entire data cleansing is a very big challenge that need to be overcome by the accountants. The use data analytics professional tools was identified as another challenge that needed the accountants to learn big data exhaustive methods.
References
Abbasi, Ahmed, Suprateek Sarker, and Roger HL Chiang. “Big data research in information systems: Toward an inclusive research agenda.” Journal of the Association for Information Systems 17, no. 2 (2016).
Boons, Frank, and Florian Lüdeke-Freund. “Business models for sustainable innovation: state-of-the-art and steps towards a research agenda.” Journal of Cleaner Production 45 (2013): 9-19.
Cai, Li, and Yangyong Zhu. “The challenges of data quality and data quality assessment in the big data era.” Data Science Journal 14 (2015).
Gandomi, Amir, and Murtaza Haider. “Beyond the hype: Big data concepts, methods, and analytics.” International Journal of Information Management 35, no. 2 (2015): 137-144.
Garonne, Vincent, R. Vigne, G. Stewart, M. Barisits, M. Lassnig, C. Serfon, L. Goossens, A. Nairz, and Atlas Collaboration. “Rucio–The next generation of large scale distributed system for ATLAS Data Management.” In Journal of Physics: Conference Series, vol. 513, no. 4, p. 042021. IOP Publishing, 2014.
Lee, Jay, Behrad Bagheri, and Hung-An Kao. “A cyber-physical systems architecture for industry 4.0-based manufacturing systems.” Manufacturing Letters 3 (2015): 18-23.
Loebbecke, Claudia, and Arnold Picot. “Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda.” The Journal of Strategic Information Systems 24, no. 3 (2015): 149-157.
Najafabadi, Maryam M., Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, and Edin Muharemagic. “Deep learning applications and challenges in big data analytics.” Journal of Big Data 2, no. 1 (2015): 1.
Soetanto, Danny P., and Sarah L. Jack. “Business incubators and the networks of technology-based firms.” The Journal of Technology Transfer 38, no. 4 (2013): 432-453.
Wamba, Samuel Fosso, Shahriar Akter, Andrew Edwards, Geoffrey Chopin, and Denis Gnanzou. “How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study.” International Journal of Production Economics 165 (2015): 234-246.
Warren Jr, J. Donald, Kevin C. Moffitt, and Paul Byrnes. “How Big Data will change accounting.” Accounting Horizons 29, no. 2 (2015): 397-407.
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