Accounting Information Systems and Development Projects

Question: Discuss about the Accounting Information Systems and Development Projects. Answer: Introduction With the due passage of time, the business proceedings have undergone a vast change. The presence of accounting information system has provided an immense boost to the system. One of the major landmarks is brought by the presence of big data that brings huge potential to the business (Caroll, 2014). In this report, the major emphasis will be on big data and its implications for the organization. To ensure an in-depth evaluation, a case study has been selected where the practical implication and challenge of big data is studied in a financial institution that is Bank of America. The case has been structured in the form of background, challenges, and benefits. Big Data Big Data can be stated as a huge data volume that is structured, as well as unstructured that cover the business every day. However, it is not the data amount that is vital. It all depends upon the organization. Big data can be evaluated to get an insight into the business that leads to better decision making and strategic motive of the business. The concept of big data gained popularity in the 2000s when the analyst of the industry spread the utility of big data as volume, velocity, and variety (Tim et. al, 2012). The benefit of big data does not consider the quantity of data rather the work that needs to be done. Data can be selected from any source and be evaluated to find answer pertaining to the reduction of cost, reduction of time and development of new product. The integration of big data along with the system of BI is a strong step towards the attainment of return on investment. Big Data, as well as business analytics, are complimentary in nature. Big data can lead to an in-depth investigational focus on the data while BI leads to a strongly structured experience of the user (Mitchell, 2014). Big data helps in reporting, as well as performance management and important when it comes to making of advanced analytics in action. Examples of commercial software that deal with Big Data: Cloudera It is among the initial Hadoop offerings and popular because of more installations as compared to other rivals. It contributes Impala that provides real-time parallel processing of Big Data to Hadoop (Mitchell, 2014). Microsoft HDInsight It is engineered to work on cloud platform of Microsofts Azure. Microsoft Hadoop dwells on Horton work and is the only Hadoop offering that functions in a window environment (Van Venzke, 2015). Pivotal HD It forms a part of the Big Data suite that contains database tools Greenplum and Gemfire (Van Venzke, 2015). It helps in the maximization of the possibilities and develops the real-time analysis. Background Big data leads to a strong business insight that encompasses traditional data from the e transactional system. When it comes to a financial institution, cost savings is much beyond the business goal that is the executive compulsion. Bank of America has huge assets size that exceeds $2.2 in the year 2012 and a humongous consumer base of 50 million and hence, tagged as the big data business years ago. The bank is stressing on big data because it leads to an approach that is integrated into nature and will lead to an organization that is integrated. The scenario for big data is thought of in three different manners that are the big transactional data, data regarding customers, and unstructured data (Thomas Davenport, 2013). As the bank had a large amount of customer data that spans across several channels and links, the historical data of the bank was difficult to be evaluated immediately and therefore, the major reliance was on the systematic samples. With the help of big data technol ogy, it can enhance the process and evaluate the data from the full customer set (Mitchell, 2014). The big data enabled the bank to understand the customers across various channels thereby enabling the bank to provide dedicated services to the customers at large. Challenges of Big Data The big challenge that Big Data brings is to know the multi-channel relationship. The customer journey is being evaluated through various websites, tellers, and various brand personnel that helps in understanding the path the customers follows through the bank and the impact of such path on the purchase of a specific financial service. Further, the sources of data on multi-channel journey of customers are unstructured or semi-structured in nature. It contains website clicks, records of various transactions, bankers note and other recordings from the call centers. In this scenario, it needs to be noted that the volume is very high (Crossman, 2012). However, the bank will understand the journey and describe them with the name of the segment that will ensure the interaction of the customer remains a top notch, the reasons for attrition can be known and customers problem can be known. This remains one of the biggest challenges because the data set is complex in the scenario but the payoff is high (Chintamaneni, 2016). Moreover, a business decision with big data for the bank even involves various other traditional areas like the risk management, supply chain, and pricing. The utilization of external data to enhance the analysis leads to issue in the big data. When it comes to supplying chain decision, banks are using ex ternal data for the purpose of evaluation and assessment of supply chain risks. Further, big data technologies require a hardware or software platform that includes the servers (parallel servers) by utilizing the Hadoop/MapReduce in terms of database processing. The evaluation that would have required hours or days can be done in few seconds. To complement the technologies of rapid nature, faster analytical techniques and machine learning technique must be put into an implementation that will produce the result at a higher rate (Chintamaneni, 2016). The challenge that arises in this scenario is to adapt to the new operational movement and decision-making process so that advantage of the technology can be taken. One problem that can arise in this case is that there is scarce of good talent. Going by the case when the company uses the new innovative technique, it will need a staff of high caliber and strong understanding. Programming and knowledge of statistics are needed to control the procedure. Additionally, profiles of data element should be created. The profile must be created to consider the clarity of record. Benefits of Big Data The banking industry rides on immense risk, therefore, every loan, as well as an investment is needed to be assessed. Big data in the above-mentioned case of Bank of America can provide an in-depth insight into the system, transaction, as well as environment that helps in mitigating the risk. Banks can assess the factors that lead to low-interest loans or invest in rebuilding. Moreover, the bank can evaluate the factors that lead to default on loans and craft new strategies that will strengthen the system (Thomas Davenport, 2013). The system can be made more transparent that will help the institutions to trace the internal, as well as external malpractices and trace the pattern of the past to prevent fraud. With the availability of data, the bank can gain previous information associated with every customer. Hence, it leads to a better view of the customers requirements and enables them to address the requirements in a proactive manner. Further, big data will bind various organizations like marketing, IT and sales to work in together. According to the situation, the bank can respond that helps in ensuring a strong financial service. Further, the presence of big data also provides up-to-date information regarding the customers who are wealthy and the pattern of choice they make. This enables the bank to provide them the services that will cater to their interest (Thomas Davenport, 2013). The performance of employees can even be done and branch budget can be made with the help of it. The past achievements can be taken into consideration for achieving the goal. Additionally, it can be used to target the concept of training and educate the employee in terms of achievement of the goals. Bank s can even use performance data regarding products, features, and services that provide new offerings in tune to the demand of the customers. Banks can easily plan for the future course of action. Going by the internal and external data availability, the bank can trace the pattern, address the problems and hunt for goals that enhance upon the metrics of history. When graphs, chart, and animation are used, interfaces of customized type enables the users to check the data. Queries can be run by the managers and reports must be pulled as per the needs (Shanker, 2014). The percentage of loans can be evaluated by monthly, type and other operating expenses. Conclusion The above discussion clearly indicates that big data provide immense solidity to the business. The case study of the Bank of America is a glaring example where the presence of big data leads to cost savings and enhance the system of transaction. This, in turn, leads to effective by the bank that caters to the requirement of the business. The advantages of big data are manifold and along with the benefit come major challenges that need to be faced to ensure the high yielding result. The work culture and the capacity of the business have undergone huge change with the availability of big data. References Carroll, J.M 2014, Computer security, Butterworth-Heinemann. 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