Žiga Vaupot's Blog

Oracle Analytics has been publicly available for more than three years now. Since the beginning Oracle has been improving the product and many innovations have been into the product stack, expanding into areas like data visualization, data discovery, data management, augmented analytics, machine learning and others quite heavily.  The gap between Oracle Analytics and its predecessor Oracle Business Intelligence has been widened and many Oracle Business Intelligence users have decided to upgrade to Oracle Analytics. On the other handside, we can still see a lot of Oracle Business Intelligence users continuing to use Oracle Business Intelligence.  I think that today, Oracle Analytics has matured enough and is providing so many new features that the time has come for all existing users of Oracle Business Intelligence to seriously consider a move to Oracle Analytics, either that being migrated and implemented within Oracle Cloud Infrastructure as Oracle Analytics Cloud, or on-premises as O

Analytics and Data Oracle User Community ( AnDOUC ) Winter TechCast Days  took place between 15th and 17th Februrary 2022.  I am very honoured to make a small contribution at the  Machine Learning Day  where I presented my view on what business users should know about machine learning support in Oracle Analytics, presentation with the title  Business Users and Machine Learning in Oracle Analytics . Business Users and Machine Learning in Oracle Analytics Traditionally, the use of machine learning is in the domain of the data scientist. The latter has in-depth knowledge of machine learning methods and algorithms and strives for the most optimal preparation of machine learning models, which are then used for various analyses such as predictions, customer segmentation, anomaly detection, finding patterns and the like. Because the machine learning process usually takes a long time, even a few weeks, the data scientist is a rather “rare species”. Business users usually need results quickly a