Data analytics is different from statistics in that statistical analysis uses statistical approaches to data sample intending to gain an insight into the whole population, while data analysis entails a process of cleaning, inspecting, modeling and transforming existing data into valuable data which could be comprehended by non-technical individuals (Shatnawi et al., 2019). The data analysis process may be utilized as an input into conducting statistical analysis, since data from different sources may be joined to perform statistical analysis.
Data is futile if one cannot gain valued understandings that result in more-knowledgeable actions. Analytics resolutions deliver an appropriate way to manipulate business data. Businesses use analytics to convert raw operational data into actionable information through various forms of analytics, counting prescriptive, predictive, and descriptive analytics. There exist key differences between these types. Descriptive analytics studies information statistically to informs a person what took place in the past. It assists a business in understanding its performance by providing a framework to assist stakeholders in interpreting data. For example, within a healthcare context, if an abnormally huge sum of patients is admitted to the emergency room within a short duration, descriptive analytics informs the stakeholders that this is taking place and offers real-time information with every corresponding statistic (volume, date of incidence, patient particulars, etcetera) On the contrary, predictive analytics foretells what is most probable to take place in the future (Shatnawi et al., 2019). The concept is employed to current information to foretell what will occur someday. On the other hand, prescriptive analytics takes predictive information to the subsequent level. It proposes actions one may take to influence those results. For instance, after knowing that the disease is increasing, the prescriptive analytics instrument might recommend increasing the amount of workforce on hand to sufficiently manage the patients’ influx.
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Data analytics assists an organization to comprehend a customer’s preferences and requirements, so that companies may upsurge their consumer base and maintain the current ones with tailored and pertinent offerings of their services or goods. Companies use various types of analytics collectively to make smart choices that assist the organization (Shatnawi et al., 2019). Descriptive statistics could be in the form of information visualizations such as dashboards, graphs, reports, and charts.
References
Shatnawi, M. Q., Yassein, M. B., Abuein, Q., & Nsuir, L. (2019, December). Big data analytics tools and applications: survey. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems (pp. 1-4).
