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System Documentation And Business Intelligence

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Question:

Discuss About System Documentation And Business Intelligence?

 

Answer:

Introduction:

The organisations operating in the present century look for efficient methods to improve their operating procedures for increasing competitive advantage in the global and regional markets. For raising the margins of revenue, the international industries aim to enhance their customer base by accumulating critical information regarding the customers. This is mainly intended to reach and attract them for the product consumption (Akhigbe, Amyot & Richards, 2014). In addition, it is of crucial importance for an organisation in recording the information regarding the potential customers to reach them, whenever necessary. Thus, it has been observed that the mechanisms of evaluation and use of data software has created in the market to separate market information for obtaining an insight of the existing trends for changing their business strategies.

 

Data analysis:

The data analysis process deals with the assessment of data analytics and this is a method of cleaning, editing, modelling, examining and conversion of data accumulated from the customers. This is intended to gather useful data utilised for recommending and undertaking inferences along with assisting in the process of decision-making.

Data mining:

Data mining is a tool of data analysis, which enables in gauging the system of identifying the trends in big data sets through use of statistical tools, joints and different mechanisms of database. The intention of such type of system is to gather rightful information from the avialable data and develop the same into information, which the analysts could interpret easily. Such process takes into account data processing and inference model to ensure the accuracy and effectiveness of the accumulated data.

 

Role of data mining and data analysis in contemporary organisations:

The data analysis technique is the method of developing the outcome for data examined on the part of data pool sets. The processing system and innovative software help in undertaking this. The commercial organisations use these mechanisms and technologies related to data analytics (Alpar & Schulz, 2016). Through these methods, the organisations are capable of enhancing the business activities and therefore, they could increase their profits, service quality to the customers and maximise their marketing policies. The system of data mining and data analytics enables the organisations to improve their business performance along with gaining competitive supremacy over the competitors. The use of the equipments of data analytics enables the corporate staffs, the management with necessary pointers of performance, information regarding the customers and the business activities (El-Gayar & Timsina, 2014).

The firms have done great number of usages and thus, it is observed that many organisations extend their support relating to the utilisation of data analytics. The industries engaged in e-commerce and the companies involved in online marketing realise the customers visiting their websites via software, which is termed as the evaluation of click stream (Foshay & Kuziemsky, 2014). Such assessment techniques assist the industries engaged in providing services to realise the patterns pertaining to the page view and the options of the customers in buying any specific service or product through dependence on the routing of website.

Conversely, data mining is a group of equipment related to analytics of data. An organisation possesses the authority of using the data associated with its assets as well as customers through exploitation of the mechanism of data analytics. The procedure of data analytics has attained eminence currently and the various business firms are now considering the same. The mechanisms of data analytics are important for the firms to remain active in the market, gain an insight of the transforming market, environmental situation and transforming requirements of the tastes and preferences of the customers (Larson & Chang, 2016).

It is crucial for the management of the organisations to gain an insight of the vast volume of data before they undertake the decision-making process. The management business intelligence and the other related parties associated with decision-making are critical, as it assists in implementing the most effective decision within the organisation (Popovi? et al., 2014). By enhancing the quality of data, sales mobility, ease of access and business intellects, the organisation are able to enhance their revenues at the time of rising amount of return.

Useful information is provided to the management for executing effective procedure of decision-making. For accomplishing this, various sources are exploited to filter and obtain information by using the different data analysis tools. The utilisation of effective and correct tools for data analysis helps in obtaining knowledge about the buying behaviour of the customers, expansion opportunities, needs of the customers and increase in competitive edge (Rajnoha et al., 2014).

Various equipments of data analysis are present in the market and it assists in the report construction through exploitation of the functional characteristic of data evaluation. Thus, it assists in communicating with the data available within the report. Data mining is an identical procedure, since it assists an organisation to obtain closer insight into the pattern of the organisation and the consumer trend. It is necessary for the firms to gain knowledge about the data analysis function associated with the decision-making mechanism by relying on the development of opportunities for the organisations.

The data mining process uses different outlooks to assess the necessary information. It takes into account the synopsis of data utilised on the part of the organisations for reducing the related costs and increasing the profits for the organisations. The users of data mining and data analytics possess the capability of investigating the information from different degrees through diversification and construction of the synopsis of relationships, which have been realised (Selene Xia & Gong, 2014). A large level of databases and numerous formats is gathered on the part of the organisations in the current era to analyse the process of data analysis. The associations have been realised coupled with relations and trends, which are useful for the development of data that are usable in nature. For instance, the products expected to be sold and the accurate selling time is projected through analysis of the information available from the information of transaction from the point sale perspective.  

The associations between the different internal factors like positioning of product, price, staffs skills and the external factors such as demography, economic indicators and market competition could be recognised through data mining. Hence, the organisations are able to recognise the effect of revenue, sales and consumer satisfaction. The information of transactions, which is particular, has been observed with the synopsis of information through drilling of the results (Sherman, 2014). 

 

Identification of ethical implication of accumulating and storing customer information:

The ethical responsibilities of an organisation are necessary for maintaining and controlling the consumer database. Such accountabilities affect the customers in trusting an organisation (Trumpy et al., 2015). The construction of ethical responsibilities is made, since it aids in investigating the databases of the customers from three different outlooks falling under the liabilities, which are distributed among the consumers and the staffs of an organisation (Vukši?, Bach & Popovi?, 2013). This is even observed that the organisations are functioning in the economy in protecting and assuring the database of the customers of being distributed to the other sources, in which this information could be utilised ineffectively. The constituent of support from the customers even declines under the ethical accountability.

This is the duty of the customers in managing their databases in handling their databases by reviewing the service or product purchases and returning such services or products provided on the part of the organisations. The staffs of the firms are hurdled in an ethical manner to avoid disclosure of the customer information until it is essential for the concerned organisation or the consumers (Yeoh & Popovi?, 2016). Thus, it is the duty of the organisations to avoid disclosure of the private information of the customers to the other parties and they need to offer accurate and precise data, as required on the part of the customers. The employees even view the same that there is accessibility of general information and concealment of personal information from exploitation. This is one of the primary liabilities of the staffs.

It is not possible to curb the initiation of any innovative, new ideas and mechanisms into organisations. However, this does not restrict the revision and transformation of the privacy policies. As a result, it might have effect on the association among the organisations and the customers at the inference, which is not beneficial. The degree related to transparency minimises with the initiation of difficult and complex products; however, transparency could be maintained, in case; the policies are converted frequently. Henceforth, it helps in maintaining the faith and trust of the customers and the controlling and maintenance of associations. Thus, it is essential that the ethical application is made at the time of collecting customer data.

 

Conclusion:

This paper depicts the data mining process and the tools of data analytics, which are necessary for the collection and preservation of customer-related information. The report identifies the lucidity, which plays a significant role and thus, it is the firms’ responsibility of informing the customers about the portion of information really utilised. This is mainly intended to minimise the monetary expense and time and therefore, it increases the satisfaction level of the customers.

 

References:

Akhigbe, O., Amyot, D., & Richards, G. (2014, October). A framework for a business intelligence-enabled adaptive enterprise architecture. In International Conference on Conceptual Modeling (pp. 393-406). Springer International Publishing.

Alpar, P., & Schulz, M. (2016). Self-Service Business Intelligence. Business & Information Systems Engineering, 58(2), 151-155.

El-Gayar, O., & Timsina, P. (2014, January). Opportunities for business intelligence and Big Data Analytics in evidence based medicine. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 749-757). IEEE.

Foshay, N., & Kuziemsky, C. (2014). Towards an implementation framework for business intelligence in healthcare. International Journal of Information Management, 34(1), 20-27.

Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700-710.

Popovi?, A., Hackney, R., Coelho, P. S., & Jakli?, J. (2014). How information-sharing values influence the use of information systems: An investigation in the business intelligence systems context. The Journal of Strategic Information Systems, 23(4), 270-283.

Rajnoha, R., Kádárová, J., Sujová, A., & Kádár, G. (2014). Business information systems: research study and methodological proposals for ERP implementation process improvement. Procedia-social and behavioral sciences, 109, 165-170.

Selene Xia, B., & Gong, P. (2014). Review of business intelligence through data analysis. Benchmarking: An International Journal, 21(2), 300-311.

Sherman, R. (2014). Business intelligence guidebook: From data integration to analytics. Newnes.

Trumpy, E., Bertani, R., Manzella, A., & Sander, M. (2015). The web-oriented framework of the world geothermal production database: a business intelligence platform for wide data distribution and analysis. Renewable Energy, 74, 379-389.

Vukši?, V. B., Bach, M. P., & Popovi?, A. (2013). Supporting performance management with business process management and business intelligence: A case analysis of integration and orchestration. International journal of information management, 33(4), 613-619.

Yeoh, W., & Popovi?, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science and Technology, 67(1), 134-147.

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