What do you mean by Data Analytics?May 22, 2019
It is the process of exploring data sets so as to draw conclusions regarding the information they have with the use of software and specialized systems. The techniques and technologies of data analytics are used widely in commercial industries to allow organizations to make more informed business decisions in order to disprove and substantiate scientific model hypotheses and theories. Data Analytics mainly refers to the mixture of applications like BI (Business Intelligence), OLAP (Online analytical processing) into multiple advanced analytics forms. It has a similarity with Business analytics, but business analytics uses business, whereas data analytics’ focus is broad. Data Analytics’ capability can help improve operational efficiency; businesses’ revenue increase, customer service efforts and optimize marketing campaign's response to emerging market trends quickly and obtain, over rivals, a competitive edge - ultimately with the goal of improving business performance. Depending on the specific application, the analyzed data can consist of either new information or historical records which, for real-time analytics, have been processed.
Data Analytics and its Types
There are at least four types of Data Analytics. Given below are the detailed descriptions of various types of Data Analytics.
1. Descriptive Analytics: The question of what happened is answered by Descriptive Analytics. For example, a provider of health care will know the number of patients that were hospitalized the last month; retailer knows the volume of average weekly sales; manufacturer knows the products rate which is returned for the past month. Descriptive Analytics organizes raw data from various data sources in order to give precious insights into the past.
2. Diagnostic Analytics: The question of why something happened is answered by Diagnostic Analytics. Thanks to the diagnostic analytics, there is a chance to drill down, identify patterns and find out the dependencies. Companies choose Diagnostic Analytics because, for a specific problem, it offers in-depth insights. At their disposal, a company should contain detailed information or else the collection of data may be time-consuming for every issue.
3. Predictive Analytics: The question of what will likely happen is answered by Predictive Analytics. Predictive Analytics uses the findings of diagnostic analytics and descriptive analytics in order to detect clusters, expectations, predict future trends and tendencies which, for forecasting, makes it a valuable tool. Notwithstanding many advantages brought by predictive analytics, it is necessary to understand that forecast is an estimate whose accuracy depends highly on stability and data quality of a situation; therefore, continuous optimization and careful treatment is required. Thanks to the predictive analytics for enabling a proactive approach, for instance, a telecom company can recognize the subscribers who would most likely reduce their spending.
4. Prescriptive Analytics: The question of what action can be taken in order to take advantage fully or eliminate the problem of promising trend is answered by Prescriptive Analytics. For example, a multinational company could identify opportunities for purchases that were repeated based on sales history and customer analytics. Prescriptive Analytics uses standard technologies and tools like algorithms, machine learning and business rules which make it standard to manage and implement.
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