Accounting Information System Data Mining â€Myassignmenthelp.Com

Question: Discuss about the Accounting information Systemfor Data Mining. Answer: Role of Aata Analysis Tools and DataMining in Contemporary Organisations Raw data helps companies to process their day to day activities in a standard form. However, raw data is cannot be presented in result form. The structure of raw data is processed to derive results. There are numbers of transactions occurred in a business on daily basis and hence the data are stored in a standard form so that it can be processed and reports can be prepared for management reporting. All the transactions and information are stored in a database and used to prepare standard reports. Statistical analysis of data is a complex and multi-dimensional task. It requires a proper and deep knowledge to analyse and use the data to generate useful information for the business (Williams, 2011). It is important for businesses to always attentive towards the data and has command on the changing demand of the customers and the rapidly changing business environment. Businesses require reacting prompt and proactive in the current dynamic business environment so that it can sustain and s urvive in the market. Decisions are made on the basis of reports which are derived from the collected data. Managers are required first to have deep knowledge of data so that they can take appropriate and feasible dictions for the business. Data analysis tools help the businesses to store data in a standard from and maintain useful database for the businesses. It is not easy to memorise each and every transaction of business and therefore, the need for data analysis tool has increased (Wang, 2008). Both data analysis and mining are the most important part of business intelligence and are widely used for successfully running of businesses. Being an inseparable part of business a strong data warehousing strategies are required for the proper functioning of these verticals. Data analysis tools are more simple than data mining tools and require specialised staff to handle it. Whereas, data mining needs to be done by experts as the results are difficult to interpret and require other methods to verify the results (Wiley Ben, 2017). Data mining is a process of analysing and extracting from many perspectives and dimensions. Data mining is of two types first is descriptive mining which emphasises on existing data, second is predictive, which emphasises on data forecasting. It is a collection of the method which is used to develop interfaces from stored data (Maimon Rokach, 2010). The prime objective of data mining is concerned to gather the information and classify it into standard formats so that it can be used in future. The basis of data mining is decision rules and tree without which data mining is incomplete. These methods are being developed for pattern recognition and for statistic learning. Whereas data analysis is the use of different analytical tools and methods to convert the collected data into a useful form. It helps to develop solutions and verify the hypothesis. Online Analytical Processing (OLAP) becomes very popular data analysis tool which helps the companies to prepare useful and accurate repor ts from the stored data. OLAP provides servers which organise the data of the companies into multidimensional hierarchies, called Cubes which enables to companies to analyse their data with high-speed. OLAP provides the information into a graphical and tabular form which can be manipulated extensively (Management Association, 2015). Cut throat competition and global presence of the companies paced the popularity of data analysis and data mining tools. Data analysis tools are like a well of knowledge for the businesses which stores the all the information and make them available for the managers. It also helps to make prompt and profitable decisions (Wiley Ben, 2017). Data mining and analysis have become an important sunset of business intelligence which also helps in database management and warehousing. Information technology has brought revolution in the business as manual recoding of transitions has now become obsolete and companies have made business process fully automated. Successful operations of global companies are due to their data mining and analysis tools. Data analysis tools are widely used to analyse the pattern of customer behaviour. Collected data are used to analyse the to discover the movement and buying behaviour of the customers which helps the businesses to predict future demand of the custo mers for products or services. In order to deal with the mountains of data, businesses store data from various sources and summarise such data in a standard reporting form. Data are gathered from both internal and external sources. Data mining enables to arrange these data into more organised and accurate form to prepare reports which are used by the management to take business decisions related to its products or services, operations and buying behaviours of its customers (Williams, 2011). Organisations need tools to extract useful information from the stored data. Data analysis tools help to bring the stored data into a single format. Raw and stored data are converted into useful formats with the help of data analysis tool. Data analysis includes simplifying, classification and reporting the data into a standard format (Maimon Rokach, 2010). Data mining and analysis facilitate the businesses to synchronise with the current market demand and customers needs. It also suggests scope for improvement to the management and takes appropriate decisions. Data analysis and mining are a qualitative technique to evaluate the business performance. Decision-making process now becomes convenient with the help of data analysis tool. Success or failure of a company is fully depending on the decision making of its management (Dubitzky, 2008). Ethical Implications Around Gathering, Storing and using Customer Information Ethics is an integral part of every business as it ensures the honesty and commitment of business towards its customers. Ethics guarantees the quality, freshness and originality of gathered data so that the decisions based on such data proves more profitable and feasible for both company and its customers. Followings are the ethics of gathering data: Minimal Risk: This is very important aspect of data gathering which ensures that the collected data will not expose any harm to anyone mentally, physically and emotionally. Informed Consent: Participation must be from free consent. One should not be forced or coerced to participate in the data collection or provide any kind of information. Participants must be informed about the usage of their given information before taking their responses. It is the duty of the researcher to acknowledge the queries of the respondents and disclose them about the usage of their responses. Consent of participants must be of free will. Informed consent maintains the integrity and reliability of gathered data (Maimon Rokach, 2010). Anonymity and Confidentially: Both the words have different meanings are often interchangeable. Anonymity refers to no identification of respondents on the basis of their submitted responses. It encourages the participants to being honesty in their responses. However, follow-up is very difficult as the identity of respondents cannot be disclosed. While conducting observations, or focus group surveys anonymity is not useful. Whereas confidentiality means that the respondents identity is known to the investigator but it is expected that he/she will not disclose it to anyone. While reporting qualitative data, the identity of respondents must be deleted or disguised. Data Security: it is an essential part of data gathering. Once gathered data or information must be protected and stored in the reliable source. An electronic device can be used as they are password protected and has limited access. Compensation: A reasonable compensation must be delivered to the participants for their valuable participation. Compensation must be based upon the time and efforts and risk of the participants. If there is no compensation is to be paid than the participants must be communicated prior to their participation and not forced to provide their valuable suggestion for free, until they are not ready. Amount of compensation must be communicated clearly to the participants and must be fair enough to reward their contribution (Management Association, 2015). There are eight principles of data protection which are as follows: Data must be processed fairly and lawfully. Provided data is adequate, relevant and not excessive. Gathered data is available for limited purposes and usage. Data is accurate and does not contain any biases. Data has been processed in accordance with the law. Data is not kept for longer or unnecessary time. Data is not transferred to another country without adequate protection. Data must be present in secure mode. Excessive use of technology created many threats for the companies and increased the rate of cyber crime. Valuable and confidential data get hacked which causes huge losses to the companies. Although technology made the work so easy and convenient but it also carries consequences with it. Customer information is very important data for every company as most of the decisions are being taken on the basis of this information so it is essential for the companies to keep this data confidential and secret especially from its competitors. Customer information should not be used unethically by any company and they should ensure the integrity and confidentially of such information (Kantardzic, 2011). References Wang, J. 2008. Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications. London: IGI Global. Dubitzky, W. 2008. Data Mining in Grid Computing Environments. Oxford: John Wiley Sons. Kantardzic, M. 2011. Data Mining: Concepts, Models, Methods, and Algorithms. New Jersey: John Wiley Sons. Wiley, A., Ben, O.H. 2017. CCSP (ISC)2 Certified Cloud Security Professional Official. New York: John Wiley Sons. Maimon, O. Rokach, L. 2010. Data Mining and Knowledge Discovery Handbook. London: Springer Science Business Media. Management Association. 2015. Business Intelligence: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications. Hershey: IGI Global. Williams, G. 2011. Data Mining with Rattle and R: The Art of Excavating Data. London: Springer Science Business Media.