Types of data analytics(2)
In my former article, I spoke about 2 different types of data analytics and i will explain the rest in this article.
Prescriptive Analytics: This type of analytics helps answer questions about what actions should be taken to achieve a goal or target. Prescriptive analysis techniques rely on machine learning. Prescriptive analytics assists firms in formulating solutions to operational issues based on inferences made from data. Big data is a black box, and it is never certain to anticipate the most trustworthy inputs, but it always identifies the root causes of those issues. Prescriptive analytics is useful in this situation. Prescriptive analytics provides advice to organizations on every outcome that could occur and leads to actions that are likely to maximize company outputs. Prescriptive analytics is a business improvement data analytics process that offers recommendations for "what should a firm do" to address a challenge. This method enables companies to make wise judgments in an unpredictable environment.
Cognitive Analytics: This type of analytics attempts to draw inferences from existing data and patterns, draw conclusions based on existing knowledge bases and then add these findings back to knowledge bases for future references. The most sophisticated type of analytics is called cognitive analytics, which integrates a variety of intelligent technologies like artificial intelligence, machine learning algorithms, deep learning models, and more to process information and generate conclusions from patterns and existing data. These discoveries contribute to the body of information for potential future stumbling blocks, and the self-learning feedback loop mimics human thought processes to make cognitive applications more intelligent and efficient over time.