The Implications of Big Data to Business Organization Free-Samples

Questions: 1.Introduction of Big Data2.What is Big Data? What is the Relationship between Big Data and Business Analytics? 3.Search and Provide a Case Example of a Company. Answers: 1.Introduction Big Data is a buzz word now days that involves humungous amount of data continuously coming from various sources in structured or unstructured format. The conventional data storage systems and analytical methods fail to deal with Big Data and Big Data analytics. Therefore Hadoop framework was created to handle Big Data. Big Data has found usefulness in auditing where it can be used for fraud detection and prevention. BDO tax advisory firms and international network of public accounting started using Big Data to reduce the errors in manual jobs so that frauds can be accurately detected. Though there are challenges in using Big Data in auditing, but benefits are aplenty. 2.Big Data We are in a pool of data today (Elgendy Elragal, 2014). In a broad range of applications, data is generated and collected continuously at an unprecedented scale by thousands of sources. The data sets are so large that the conventional storage methods fail to store such humungous data. However Big Data is a relative term. For a static application that deals with limited MB (Megabyte) of data, even 100 GB (Gigabyte) of data is Big Data while for a social media site such as Face book that has billions of users globally something in the range of Exabyte (1018 bytes) would qualify for Big Data. Volume however is not the only criteria to classify the data as the Big Data. The other characteristics of Big Data are Variety, Velocity and Veracity (Williamson). Variety is about the data being in different formats. The data comprises audios, videos, texts, and photos etc that require different storage methods to save the data. Managing stagnant huge amount of data is still relatively easy. In case of real Big Data, the data is continuously flowing like a river.(Annoynomous, 2015) gives a bird eye view of live data being exchanged in a second over the internet. Veracity talks about the quality of the data that is being generated. It is not necessary that all the data that is generated could prove useful. Big Data without conveying any information is as good as trash. Therefore it requires the use of business analytics knowledge to convert the data into information. The conventional statistical and analytical softwares fail to store and analyze big data owing to its 4Vs characteristics. (Martinek Stedman, 2014)Hadoop framework was launched by Apache in 2006 to handle Big Data. By 2011 as big data gained momentum various organizations that analyze big data and collect process turned into Hadoop, Yarn, Map Reduce, Spark, HBase, Hive as well as NoSQL databases. Primarily Hadoop clusters and NoSQL are used as the launching pads to store big data before it gets loaded into analytical database. Statistical analysis and text mining plays important role in big data analysis with the BI software and data visualization tools. Analytics application and ETL applications cab ne described in batch mode. 3.Case Study BDO, an international network of advisory and tax firms as well as public accounting that provides professional services across 154 countries. As a part of its professional services, BDO provides auditing services to IT firms that aim to assess, detect and prevent risks and frauds in IT firms. Currently fraud auditing is a painstaking task that requires an auditor to perform brainstorming sessions with the team, discussion with the management about their knowledge of fraud(Trang, 2011), suspicion of fraud, awareness of allegations of fraudulent financial reporting, hours long interviews with the numerous employees and other stakeholders and manually going through the various kind of internal data(O'Neill, 2015). This involves lot of manual work, requires a team of resources and is a time consuming process. Big team and time converts into money i.e. the whole process proves costly to the firms and since it mostly involves human interaction at every level, the process is prone to error s. It is highly possible that the auditors are unable to detect frauds when they are actually happening. Secondly as humans sometimes it is impossible for the auditors to analyze each possible mode through which data can be collected that can prove useful to them in fraud detection. They focus on the limited information presented to them. As a result need of Big Data Analytics have been realized in the auditing and accountancy that would help the firms to gather the data from various sources in structured or unstructured formats and analytics would assist in fraud detection and prevention? However the road to Big Data is not easy. Big Data Challenges To accounting and finance professionals big data provides the possibility of reinvention along with they can take a chance to take a more strategic and future facing role in the Organization(Cokins, Tan, Boomer, 2013) The transition however is not easy. The professionals who want to stand out from the crowd who can develop new ways of thinking and develop new skills. This will require people to get out of their comfort zone and get acquainted to the latest technologies. Apart from developing new analytical capabilities, auditing professionals will have to develop new metrics to detect and prevent frauds and risks and create a visual language of data art that would help the professionals to easily identify the frauds through deviations and outlier detections. Big data requires vast amount of data in various formats to be collected, stored and transferred through technological means. Getting the big data right can facilitate the performance, productivity and can yield high end results. Therefore data management is a big challenge in leveraging Big Data capabilities in auditing to use the data strategically and unlock the data potential.(Alcides, 2016) When Big Data management and Big Data analytics are discussed, it is imperative that technology is discussed as well. Leveraging Big Data potential requires upgrading IT infrastructure, bringing technical specialists on board to setup the whole system. This need large amount of investment therefore implementing Big Data solutions is a strategic decision that requires discussions from various aspects and long term benefits have to be kept in mind during such discussions. One other important challenge that Big Data face in auditing and accountancy is ethics and data privacy(Katz, 2014). Fraud detection would require surveillance of employees such as tapping of their mails, time they spend on internet, sites visited, telephone calls made, and all the text floating among the employees within an organization. This poses a threat to employees privacy at work, and loss of data security to selected personnel. However some contend that collection of data through corporate resources within a corporate for a corporate purpose is a fair game for the efforts of corporate accountants. Big Data Benefits The challenges that Big Data face in the industry can be countered by the benefits and the ROI that it brings. Auditors work involves flipping through hundreds of documents, reading thousands of mails, interviewing people for hundreds of hours(O'Neill, 2015). Big Data allows the auditors to look at the dark corners of the rooms that have been ignored in the past and actually can prove useful in detecting the frauds. All this data is in unstructured format and sometimes uninterruptable by a naked eye. Auditors can be benefited through the use of Big Data and analytics as it can bring all the structured and unstructured data together into a single platform that can be crunched and analyzed and used in fraud detection. The other benefit of using Big Data and Big Data analytics is repetition. The models used in Big Data can be reused for multiple cases and similar analytics techniques can be used that would save time, labor and money. Repeating the same models in multiple cases reduces the team members labor of doing the same job again and again from the scratch and with time, saves the money.(Baechle, 2017) Big Data analytics allow the information to be visualized from various sources like social media, emails or any official document. This process helps auditors and accountant professionals can see the level of frauds. Visualization is an easy way of detecting the outliers or the suspicious activity. When they have to deal with huge data it helps in identifying anomalies and trends. Using the right Big Data tools, impact on response time is also high. Data involving millions of points can be analyzed quickly in hours using Big Data tools otherwise which would take weeks or months to analyze.(Navroop, 2017) BDO Consulting Director Kirstie Tiernan said in one of her consulting case involved thousands of vendors and they were to determine which one was causing the discrepancy. They leveraged Big Data capabilities and analytics to cut a list of vendors from thousands to a dozen and from that place they can see the data for any inconsistencies. There was one vendor that had tons of inconsistent data and other clues that was used to zero on to that vendor. It was the use of Big Data analytics that helped BDO to quickly analyze such huge data sets and determine the irregularities in data. If they had been interviewing all the vendors and using random sampling as they did in the past, the probability to have found that specific vendor would have been very low (Brown, 2016). Conclusion The benefits of Big Data cannot be ignored in auditing and accounting firms where the firms are leveraging the potential of Big Data to save time reduce costs and minimize the errors in fraud detection. Accurately detecting the frauds can mitigate the risks for an organization and lead to increase in productivity. Though there are challenges that Big Data bring to the table such as putting in investment to upgrade IT infrastructure, hiring Big Data technical specialists, educating professionals on how to use Big Data technologies and analytics, but the benefits and long term Return on Investments are quite high that would help the organizations and its people. Bibliography (n.d.). Retrieved from https://en.wikipedia.org/wiki/BDO_International Alcides, F. (2016). Prototyping a GPGPU Neural Network for Deep Learning Big Data Analysis. Annoynomous. (2015). Retrieved April 23, 2017, from www.internetlivestats.com: https://www.internetlivestats.com Baechle, C. (2017). Big Data driven co-occuring evidence discovery in chronic obstructive pulmonary disesse. Journal of Big data . Brown, J. (2016, March 14). Retrieved from https://www.firstinsight.com/press-coverage/5-ways-companies-are-using-big-data Cokins, G., Tan, G., Boomer, G. (2013). Big data: its power and perils. The Futures Company. 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