Organizations are benefiting from use of business analysis processes. Data analysis helps organizations establish areas that should be targeted to promote growth. By making use of analytics, organizations can make better decisions, minimize wastes through determination of optimum spend, implementing appropriate intervention efforts, determining right conditions and areas of investment, and tracking of performance (Hammer, 2018). The implementation process involve mapping context and identification of key stakeholders that required. The project will be conducted through the operations department. The organization will employ data analysts who will be involved in collection and analysis of data collected in specific organizational units. The analytics will then be employed by various departments to ensure appropriate budgeting, track employee performances, determine products to be stocked, and predict market outcomes.
The organization will make use of application software as tools for data analytics. The specific tools to be used include IBM Watson analytics and Tableau Public (Sullivan, 2016). The applications will be of essence in collecting data from business systems that will then be channeled into a repository that will combine all data collected. The repository acts as a data warehouse where information can be retrieved and analyzed (Sanders, 2014). Implementation of the analytics will involve determining organization’s policy objectives, mapping the context, identification of critical stakeholders, identification of purposes for analytics process, developing a strategy, analyzing resource capacity of the organization, and developing of a monitoring system (Wamba et al., 2017). To protect data, the organization will invest in security information and event management (SIEM) products to safeguard attacks and report log entries.
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References
Hammer, M. (2018). Methodology: Implementing an Analytics, Time and Six Sigma Based Operations Management Approach. In Management Approach for Resource-Productive Operations (pp. 173-186). Springer Gabler, Wiesbaden.
Sanders, N. R. (2014). Big data-driven supply chain management: A framework for implementing analytics and turning information into intelligence . Pearson Education.
Sullivan, D. (November 2016). Introduction to big data security analytics in the enterprise. TechTarget . Retrieved from https://searchsecurity.techtarget.com/feature/Introduction-to-big-data-security-analytics-in-the-enterprise
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research , 70 , 356-365.
