ACC 539 Accounting Information Systems

• Explains the role of data analysis tools and data mining in contemporary organisations
  • Identifies and explains the ethical implications around gathering, storing and using customer information.

In the functioning of an organization, data analysis is an important aspect for the development of an organization.  Data analysis tools and data software are significantly used to classify the data of an organization in order to recognize the trends and patterns and establish a relationship(Gelman et al., 2014). Data analysis techniques are similar to data mining as they aid any organization practically to achieve more knowledge into the customer trend, organizational and industrial pattern and behavior.

During the level of application, the business intelligence and the reports aids the management of the organizations and the other corporate employees with information with the help of which actions can be taken with respect to the vital performance indicators, customers, operations of business and more(Kanehisa et al., 2014). It is seen that in the past the reports and the data queries were established for the end users.. Data mining and data analysis tools involves the classification through a large set of data and recognize the relationships and the patterns(Fernandes et al., 2015).

Data analysis applications requires more than evaluating the data. In case of advanced projects, the work takes place in the beginning with the integrating, collecting and preparing the data and then enhancing, examining and revising the models of analysis to make sure that they provide precise results(Newcomer et al., 2015). Furthermore, the data analysts and the analytics team appoint data engineers who have the job of helping the data sets to get ready for evaluation. The initiatives of data analysis aids a vast variety of uses for the organizations. For instance, banks and other financial organizations evaluate the withdrawal and the expenditure trends to restrict theft of identity and fraud. The marketing and e-commerce industries undertake clickstream analysis to recognize the visitors of their websites who have the potential to purchase their products and services(Bazeley& Jackson 2013).

The analysis process initiates with the data collection where the data scientists recognize the information they need for specific application of analysis and then work independently with other co-workers to assemble the tools for use. The information from various systems of information may be needed to be mixed with the help of data integration routines and changed into a standard format and downloaded in the system of analytics(Witten et al., 2016). The organizations are making use of such data analytics approaches as a process to obtain information to support their firm in a better way and serve their customers properly so that they are satisfied with their service.

The ability to construct multiple sources of data creates new anticipations for the development of reliable quality for instance to relevantly look for variances in the transformation velocity, granularity, perishability, life span and dependency on a specific set of data(Hibbert&Thordarson 2016). The convergence of huge data analytics leads to the generation of newer requirements with achieving the accurate information to the consumers swiftly and making sure the reliability of the extrinsic data that are uncontrollable and authenticating the relationship between the elements of the data and looking for data gaps and synergies and establishing attributions of the data provided to others and highlighting the biased and the skewed data. The role of data mining and data analysis is useful for achieving the correct information for proper functioning of the business by getting know the what is more significant to the operations of the business in order to gain better context of data.

The accurate data sets after analysis with the help of data analysis and data mining provides results that are evaluated by the management to discover whether the data and the tools are useful for getting the right answers that will influence the management to undertake decisions, which will lead to expansion and growth of the organization(Friese 2014). The other role of the data mining and data analysis involves adding value in the information technology of a business. It is significant to compute the capabilities of the business results that arise out of the services of the IT department. It is important to concentrate on the goals of the business and have knowledge of how the usage of the services of IT contribute to the establishment of business outcomes to give out the appropriate base for constructing and planning the services that will be rendered in future. IT services are relevant for proper functioning of the analytics tools and therefore, these tools upgrade the importance of IT services in an organization(Banerjee et al., 2014).

There are various ethical issues that are associated with the storage, collection and protection of the data and the information in the data bases. The organizations gather and store a pool of information with respect to the customers in their database. These issues that are ethical in nature as associated with such information in the databases and are tested with respect to three viewpoints namely ethical responsibilities of a firm towards their customers, ethical accountabilities of employees to the organization and their customers and ethical accountabilities of the consumers towards the organization(Holst et al., 2016). The collection and restoring the customer information is important to tailor the customer service program of the organization and expanding their business.

The ethical responsibilities that organizations have towards their customers rotates around the collection of the precise data from the customers, and rectifying the errors discovered in the customer data. The ethical responsibilities that are associated with the employees is to restrict browsing through the information or the records of the customers until inevitability dictates and not selling off the information of the customers to their rivals and not revealing the data of the customers to the associated parties. The customers have ethical responsibilities as well that are related to giving information or data to the organizations with which they deal. These would be inclusive of giving precise and whole data when these data are required and safeguarding the obligations for not disclosing the data or misuse the data available in the company that accessible to them(Expósito et al., 2016).

 However, there are some requirements that are legal in nature with respect what can be done with the information that have been collected by the firm. The ethical issues comprise of the complying with the privacy laws with respect to the information that have been collected from the customers. The ethics even cover the process of storing and how the information can be utilized. With respect to the collection of information from the customers, the organization tries to discover what the customers are purchasing and the reason they are purchasing and timeframe of their purchase(Kolesnikov et al., 2015). The information is collected with respect to the potential customers who have made investigations about the services and goods of their products.

The next ethical implication refers to the accuracy of the information as any misinformation may lead to fouling of the lives of the customers. The information that are collected by the organization requires to be precise and accurate so that the firm can undertake proper decision making and have proper knowledge about the customers so that they can provide accurate services by having knowledge about the requirements of the customers(Blattmann et al., 2016).

Access of the information related to the customers is another ethical implication so that the information can be accessed with ease by the data analysts and the engineers. It is of primary importance to make use that these information cannot be accessed by other employees who are not related to function with these information. The accessibility requires to be restricted in order to safeguard the personal information of the customers and misplacing these information in the wrong hands.  The data should be stored in such a way so that they can be utilized whenever necessary (Ott &Longnecker, 2015).

Ethical implication with respect to the storing, gathering and using the information is significant as it influences the customers that their information is secured.  The gathering of the information is undertaken in an ethical way so that the customers are not tortured while gaining the information and thereby the customers get satisfied. Therefore, it is seen that an organization requires to undertake such steps so that they can understand the requirements of the customers so that they provide extensive and necessary services to the customers thereby increasing their profit and  share in the market.

Reference List

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Bazeley, P., & Jackson, K. (Eds.). (2013). Qualitative data analysis with NVivo. Sage Publications Limited.

Blattmann, P., Heusel, M., &Aebersold, R. (2016). SWATH2stats: an R/bioconductor package to process and convert quantitative SWATH-MS proteomics data for downstream analysis tools. PloS one, 11(4), e0153160.

Expósito, A. G., Gomez-Exposito, A., Conejo, A. J., &Canizares, C. (Eds.). (2016). Electric energy systems: analysis and operation. CRC Press.

Friese, S. (2014). Qualitative data analysis with ATLAS. ti. Sage.

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis (Vol. 2). Boca Raton, FL, USA: Chapman & Hall/CRC.

Hibbert, D. B., &Thordarson, P. (2016). The death of the Job plot, transparency, open science and online tools, uncertainty estimation methods and other developments in supramolecular chemistry data analysis. Chemical Communications, 52(87), 12792-12805.

Holst, M., Sarbach, O., Tiglio, M., &Vallisneri, M. (2016). The emergence of gravitational wave science: 100 years of development of mathematical theory, detectors, numerical algorithms, and data analysis tools. Bulletin of the American Mathematical Society, 53(4), 513-554.

Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2014). Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic acids research, 42(D1), D199-D205.

Kolesnikov, N., Hastings, E., Keays, M., Melnichuk, O., Tang, Y. A., Williams, E., ...&Megy, K. (2015). ArrayExpress update—simplifying data submissions. Nucleic acids research, 43(D1), D1113-D1116.

Newcomer, K. E., Hatry, H. P., &Wholey, J. S. (2015). Handbook of practical program evaluation. John Wiley & Sons.

Ott, R. L., &Longnecker, M. T. (2015). An introduction to statistical methods and data analysis. Nelson Education.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.