Josphine Njeri Kiiru

 PREDICTIVE AND DESCRIPTIVE ANALYTICS Your Name Author’s Note With the constant evolution of technology use and size of workforces in organizations today, analysis of different types of data is inevitable. The intensity of analytics is more so prevalent in the Human Resources Departments, which frequently handle tasks such as data collection, evaluation, and reporting (Scott Mondore, 2016). It is, therefore, critical to understand the different types of analytics used by organizations to evaluate elements such as performance, to enable managements select their most preferred method of carrying out analytics. Descriptive analytics is the most basic and widely used type of analytics in many companies. It involves analyzing the frequencies of past occurrences and using these to come up with broad trends (Burgess, 2017). After the acquisition of data, charts are formulated and averages calculated to come up with the figures. Additionally, the data collected may be categorized depending on the company to determine the best performing groups. Although descriptive analytics play a significant role in deciding ‘what happened,’ it does not contribute much towards determining possible correction strategies. It is also challenging to predict the future of an organization’s performance using descriptive analytics since it mainly focuses on a description of what happened. However, visual analytics is essential for any business to grasp continually and monitor the performance of its employees and technologies being used in the industry, leaving no loopholes. Predictive analytics, on the other hand, is a technique used in predicting future performance based on data collected for descriptive analytics (Scott Mondore, 2016). Organizations can make informed decisions in the future and avoid past mistakes since they already have proof of what works and what fails. Predictive analytics give approximates about the possibility of achieving specific outcomes in the future, which improves an organization’s performance level significantly. It is, however, worth noting that predictive analytics depend entirely on probabilities that employ statistical algorithms as opposed to being confident. Statistical data is carefully analyzed and used to fill in possible missing gaps for future occurrences. After compilation of historical information, patterns and algorithms are developed and comparisons done between different grouped data sets to predict the future. With my interest in the field of web and application development, descriptive analytics play a vital role in the industry. For instance, in this field, it is critical to keep track of numbers of gained followers over a specified period, or the percentage of new visitors and page views. Such analytics play a vital role in the determination of elements such as product response from the market and validate if specific features or updates previously installed were efficient. In the web development industry, predictive analytics is used to develop credit scores. Given that most web application users have to subscribe to specific services to be able to use all the available features, predictive analytics are used to determine the future probability that users of the applications make such subscription payments frequently and on time. It makes it easier to predict possible monthly and annual sales, as well as future customer needs. In conclusion, it is evident that analytics play a significant role in the determination of present and future performance levels of an organization. Data analytics is an essential competitive advantage for organizations with growth and development visions. Organizations should, therefore, strive to choose the most applicable data analytic tools and methods based on their business models to cut on operation cost while enhancing performance (Shah, 2018). By simplifying data sets in an organization to meaningful action points, informed decisions can be made. References Burgess, J. (2017, March 23). Four Types of Big Data Analytics and Examples of Their Use. Retrieved February 27, 2018 Scott Mondore, H. S. (2016). Prioritization and Practicality with Advanced Analytics. From the Boardroom to the Front Line, 32. Shah, K. (2018, January 22). Types of Analytics: descriptive, predictive, prescriptive analytics. Retrieved February 27, 2018