Data Analytics can be divided based on the 5 types of questions it can answer. 1/ What happened? - Descriptive Analysis Descriptive analytics answers questions about what happened. Descriptive analytics techniques summarize large datasets to present insights to stakeholders. The presentation of data related to those KPIs is descriptive analytics. 2/ Why did it happen? - Diagnostic Analytics Diagnostic analytics helps answer questions about why things happened and is the next step in data analytics after descriptive analytics. Analysts take findings from descriptive analytics and dig deeper to find the cause. Diagnostic analytics generally occurs in three steps: - Identify anomalies in the data. - Collect data that are related to these anomalies. - Use statistical techniques to discover relationships and trends that explain these anomalies. 3/ What will happen in the future? - Predictive Analytics Predictive analytics helps answer questions about what will happen in the future. Predictive analytics techniques include statistical and machine learning techniques such as forecasting, neural networks, decision trees, and regression. Predictive analytics is often performed by data scientists. 4/ What actions should be taken? - Prescriptive Analytics Prescriptive analytics takes predictive analytics one step further and helps answer questions about what actions should be taken to achieve a goal or target. This technique allows businesses to make data-informed decisions in the face of uncertainty. Algorithmic content recommendations are a common implementation of prescriptive analytics. 5/ How can the problem be solved best? - Cognitive Analysis Cognitive analytics combines artificial intelligence, machine learning, and data analytics approaches to guide decision-making. A self-learning feedback loop, this feedback loop enables cognitive applications to become more precise over time. How many of these did you know before?