Write about the Business Intelligence Research Report.
Data mining describes the process of data extraction, data analysis from viewpoints/dimensions, and subsequent production of a summary of info in a useful manner which acknowledges the relationships with the data (Trieu, 2017). Two types of data mining are descriptive that avails info regarding available data; besides predictive, that offers projections anchored on the data. Data Analysis on the other hand describes the process that uses a diverse tools and methods already developed for querying available data, discovering exception, and verifying hypothesis and entail reports and queries, managed query environment along with OLAP (besides associated variants like ROLAP (relational), HOLAP (hybrid) and MOLAP (multidimensional)) (Trieu, 2017). Data analysis remains a significant technique for the development of knowledge from vast quantities of data on business gathered and stowed daily, business require the effective selection of tools of analyzing data. Such effectiveness will make sure that strengths of such tools are commensurate to the business’ needs (Yang, Pinsonneault & Hsieh, 2017). Firms have to master how tools are utilized and corresponding audience. Both internet and mobile users’ needs along with power users must be taken into account alongside the assessment of users’ skills along with knowledge besides the level of training required to obtain the foremost productivity from these tools.
The methodological approach adopted for this study was systematic review of existing data on the topic. The internet was used to obtain the article that when the reviewed to gather data. The design for the study was mainly exploratory qualitative study. A total of twelve peer reviewed articles were selected and review. Data cleaning and censoring was done to eliminate the overlapping data. Thematic analysis focusing on the data analysis and mining tools was then perform to make data more useful to users.
The skyrocketing quantity of data that is under the present generation per annum make accessibility of useful info from such data increasingly essential. The info stowed in the data warehouse is the data repository accumulated from numerous sources like abridged info from internal systems, corporate databases along with data from outside sources (Shen et al., 2017).
Data analysis entails simple probe along with reporting, analysis statistically, multifaceted analysis multidimensionality as well as data mining. Both data analysis alongside data mining remain key subsets of BI that further integrates Online Analytical Processing (OLAP), data warehousing and systems of database management. The above technologies are useful in Customer Relationship Management (CRM) when analyzing trends/patterns as well as querying databases.
The search and subsequent analysis of large amount of data enables the discovery of useful trends/patterns and relationships, that are in turn utilized in the prediction of future behavior. Certain estimates suggest that the quantity of newfangled info double at an interval of three years. The info is stowed in the data repository collected from diverse bases to speak to the mountains of data. The info is then designed and implemented properly and updated regularly and stowed in the warehouse thereby enabling managers to excerpt to and examine info relating to the company’s buying habits of customers, operations as well as products.
Both tools for analysis and mining of data utilize quantitative examination, recognition of pattern/trend, analysis of cluster, discovery of correlation along with associations to undertake data analysis with slight or no Information Technology involvement. The aftermath information is in turn presented to a given user in a form that is effortlessly understandable via BI process. Analytical tools available for managers include queries and reports, OLAP alongside its variants MOLAP, ROLAP and HOLAP or managed query environments. Data mining supports such analytical tools as it develops trends/patterns useful for future analysis, and competes the process of BI.
The tools for Data Mining utilize a range of techniques like advanced statistics and neural networks thereby allowing the locations of trends/patters acknowledgeable in data and in turn establish hypothesis. Data Analytic tools like enquiring tools besides OLAP variants examine data, undertake relationship determination, and subsequently perform hypothesis testing relating to the data. The Data Analytic tools endure to develop as well as grow under this background, with the entire goalmouth of enhancing BI, improvement of decision analysis, and, further lately, connections with business process management (BPM/workflow) promotion.
Many ethical issues regards the gathering, storage as well as protection of data in respective databases. Organizations gather and stow a wealth of info relating to customers in corresponding databases. Three perspectives enable the effective examination of issues linked to data collected and stored in databases. These standpoints encompass companies’ ethical responsibilities to its corresponding customers, employees’ ethical responsibilities to organization and its corresponding customers and customers’ ethical responsibilities to the organization.
The companies’ ethical responsibilities to customers circumvents around the data gathering of solely essential data from the customers, correctly safeguarding customer data, restricting the sharing of the customer data, as well as censoring errors in the customer data. the employees’ ethical responsibilities is to evade browsing via records of data and customer unless it is dictated by necessities, never selling the customer data to rivals, and never disclosing customer data to associated parties.
The ethical responsibilities of the customer relates to their provision of data to the organization they are in a dealing with. Such customer responsibilities to the organization will entail provision of accurate as well as complete data where the data is essential, as well as upholding the obligation never to disclose or utilize the company data they can access (Marjanovic & Dinter, 2017).
The companies’ data analysts that use web analysts via digital measurement tools like Google on the websites of their client must have their Web Analysts Code of Ethics adhered to strictly. The professional must engage only with organizations that keep their data confidential, private as well as protected (Vidal-García, Vidal & Barros, 2017). The companies must provide full disclosure of their corresponding consumer data usage practices to their respective customers, encompassing if and when they sell such data to 3rd party vendors. The ethics for data buyers must also be adhered to where certain organizations purchase data from additional sources in determining marketing strategies, targets of sales as well as discrimination of prices.
The principles of data protection must as well be adhered to strictly. Eight principles of protecting data must be complied with by people processing the data. The analysts must fairly as well as lawful be processed and utilized for limited purposes. Further, data has to be adequate, relevant as well as non-excessive and accurate (Visinescu, Jones & Sidorova, 2017). The data should as well never be kept longer than necessary as well as processed according to data customer’s rights. The data must as well be secure and never transferred to nations without sufficient protection.
The customers from whom the data is collected must have informed consent. These customers must have adequate info to make independent choice of whether or not to partake that is oriented on comprehension of the risks as well as alternatives in surrounding that is free from coercion. The potential subject’s decisions on the issue of consent has to be evidenced (Fuchs, Höpken & Lexhagen, 2017). The subject requires to have an agreement that her data shall be utilized for a particular study scope as well as aware of the meaning of such utilization.
Conclusion
Whereas tools for data analysis are increasingly becoming simpler, extra sophisticated techniques shall need specialized staff. Particularly, data mining will need extra expertise since outcomes can be challenging to interpret and, hence, could need verification utilizing additional methods. Both analysis and mining of data remain integral components of BI, and need firm strategies for data warehouse to function properly (Fink, Yogev & Even, 2017). This revelation implies that additional attention should be focused to the ETL mundane aspects and advanced analytical capability. The end product can solely be as effective as data which nourishes such a system.
Provided the central repository to store the huge quantities of data, firms require tools which aid in the extraction of the significant useful info from a given data set hence the need for data analysis tools and data mining. Data analysis must entail simple functions of probe besides reporting, analysis statistically and sophisticated analysis of multidimensional data along with data mining (or knowledge discovery in database/KDD).
References
Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management, 54(1), 38-56.
Fuchs, M., Höpken, W., & Lexhagen, M. (2017). Business intelligence for destinations: Creating knowledge from social media.
Kokina, J., Pachamanova, D., & Corbett, A. (2017). The role of data visualization and analytics in performance management: Guiding entrepreneurial growth decisions. Journal of Accounting Education.
Le-Khac, N. A., Kechadi, M., & Carthy, J. (2017). ADMIRE framework: Distributed data mining on data grid platforms. arXiv preprint arXiv:1703.09756.
Marjanovic, O., & Dinter, B. (2017, January). 25+ Years of Business Intelligence and Analytics Minitrack at HICSS: A Text Mining Analysis. In Proceedings of the 50th Hawaii International Conference on System Sciences.
Roiger, R. J. (2017). Data mining: A tutorial-based primer. CRC Press.
Shen, C. C., Chang, R. E., Hsu, C. J., & Chang, I. C. (2017). How business intelligence maturity enabling hospital agility. Telematics and Informatics, 34(1), 450-456.
Shmueli, G. (2017). Analyzing Behavioral Big Data: Methodological, practical, ethical, and moral issues. Quality Engineering, 29(1), 57-74.
Trieu, V. H. (2017). Getting value from Business Intelligence systems: A review and research agenda. Decision Support Systems, 93, 111-124.
Vidal-García, J., Vidal, M., & Barros, R. H. (2017). Computational Business Intelligence, Big Data, and Their Role in Business Decisions in the Age of the Internet of Things. In The Internet of Things in the Modern Business Environment (pp. 249-268). IGI Global.
Visinescu, L. L., Jones, M. C., & Sidorova, A. (2017). Improving Decision Quality: The Role of Business Intelligence. Journal of Computer Information Systems, 57(1), 58-66.
Yang, J., Pinsonneault, A., & Hsieh, J. J. (2017, January). Understanding Intention to Explore Business Intelligence Systems: The Role of Fit and Engagement. In Proceedings of the 50th Hawaii International Conference on System Sciences.
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