What is OLAP in Big Data
BI mainly deals with events in the past and their effects on the present. It answers questions about what happened (what happened when?), The amount, frequency or the causes of an event. Tools for this are, for example, reporting (KPIs, metrics), automated monitoring (alarm when threshold values are exceeded or not reached), dashboards, ad-hoc inquiries or OLAP (Online Analytical Processing). OLAP, for example, works deductively, so it creates hypotheses and asks for specific information in order to confirm or reject the assumption.
Predictive analytics as a sub-discipline of business analytics
Business Analytics extends BI to include a look into the future and primarily relies on statistical analyzes of company data. Business analytics provides answers to questions about the reasons, effects, interactions or consequences of events. It is also possible to run through scenarios and show alternative courses of action: What happens if we turn this or that adjustment screw?
Business analytics uses various analysis tools to improve the planning process in the company. Here are the most important applications of business analytics including methods:
• A / B testing or multivariate testing with multiple variables to verify decisions.
• Statistical or quantitative analysis to explain why a certain outcome occurred.
• Discovery of new patterns and relationships in data (data mining). In contrast to OLAP, for example, this analysis technique is inductive, i.e. it searches for abnormalities or patterns in the data sets without any prior assumption, interprets them and creates its own hypotheses.
• Predicting future results, i.e. predictive analytics.
Predictive analytics is a sub-discipline of business analytics. It starts where OLAP or reporting leave off. Instead of just analyzing the existing situation, predictive analytics tries to make predictions about possible future events with the help of data models. There is a close connection with data mining.
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