Provide a reflection

Running head: TECHNOLOGY APPLICATION

TECHNOLOGY APPLICATION 2

Technology Applications in Enterprises

Student Name

Institution

Course

Instructor

Date

Technology use in enterprises has resulted in an effective process through use of certain techniques in implementations of strategic management. In big data mining, the use of technology has led to massive computations of analysis, eased the processing of data and increased accuracy in the analysis (Tran, 2016). Different technical skills and knowledge in management techniques have improved the organization's processes and made them more effective. Organizations can improve their efficiency and make their operations effective through applications of technological skills in clustering analysis, classification methods, and big data analytics.

Skills in classifications methods have led organizations to organize their data effective and this has facilitate the protection of data from loss.Skills in classification methods have enhanced data protection through appropriate planning and organizing enterprises data to their agreed categories, this has prevented loss of critical loss of data through the wrong classification. Additionally, knowledge in classification methods enables a company to identify research areas to get particular data of the organizations (Paulheim, 2017). Classifications enable easy access of company’s information through an understanding of the research zone for particular data, this reduces, on time wastage and improves efficiency in the enterprise's operations.

Knowledge in clustering analysis to the organization helps companies to understand and identify distinct groups of customers in the company. The skills from clustering analysis are useful in the identification of nasty behaviors such as fraudulent practices. for example, knowledge in clustering analysis in insurance companies may be useful in times of identifying false claims from fraudulent individuals.knowledge of different cluster methods is useful to an individual in that it helps one to choose the appropriate cluster for data analysis depending on the amount of the data in the company (Cooke &Huggins, 2018). For example, when the company has high amounts of data, an individual with clustering analysis skills would prefer choosing k-means cluster analysis for large data over other clusters.

Furthermore, knowledge in big data analysis is helpful to the organization to help in the extraction of its useful extraction form various classes of data. The big data knowledge helped organizations to new opportunities such as markets of their products and improve their strategy managements (Abbasi, Sarker & Chiang, 2016). Skills in data analysis acts s a basis for consideration during a time of making decisions of the company.technology in big data has also enhanced speed in organizations operations such classification and data analysis. Through data analytics business are able to make new pattern through established relationships that are brought about by the insights from the analysis.

Therefore, technological skills such as classification methods, clustering analysis and knowledge about big data analysis have been useful to organizations through improving effectiveness .this skills have led to proper strategical management through the implementation of new techniques to identify new markets and to identify any discreet behaviors in transactions and in some customers. These skills have also ensured the security of enterprises data through proper classifications of data to their different classes.

Referencing

Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), I.

Cooke, P., & Huggins, R. (2018). High-technology clustering in Cambridge (UK). In The institutions of local development (pp. 63-84). Routledge.

Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic web, 8(3), 489-508.

Tran, T. T. (2016). Enhancing graduate employability and the need for university-enterprise collaboration. Journal of Teaching and Learning for Graduate Employability, 7(1), 58-71.