Žiga Vaupot's Blog

Many companies which are using Oracle Business Intelligence (OBI) for business analytics are faced with the dilemma of how to advance their analytics solutions and which business analytics tool strategy should they define and follow. Decision what to do usually has a long-term effect and is not always easy.  In case of OBI, there was quite a lot of uncertainties for several years. The tool itself didn’t see many changes and (major) updates. If there were any, such as Mobile Apps Developer (MAD), these were not particularly successful and have proved as "dead end". Oracle BICS (BI Cloud Service) was also a kind of failure as it was never became a successful product. It is not a surprise that many OBI users, even though they invested a lot into Dashboards or BI Publisher reports decided to abandon OBI in favour of new tools such as such as Tableau or PowerBI.  Oracle Analytics is a successor of Oracle Business Intelligence It is funny, but I’ve spoken to many OBI users who were

Oracle Analytics  September 2022 Update  has just came out. At least our instance got patched over the last night.  There are two major updates in this release, which I plan to cover in my future posts: The first new feature relates to the usage of REST APIs . Oracle Analytics can now connect to REST API data sources , such as Saas or PaaS applications. This feature is still in Preview, but available. Speaking of REST APIs, there is another nice feature that addresses snapshot management and data loading .  Snapshots (BAR files) can be now managed   programmatically using REST APIs. Similarly developers can programatically load their data for datasets using REST APIs. I believe both features will be welcomed by developers community. Even bigger, really MAJOR update is Semantic Modeller which will one day eventually replace good old BI Administration Tool . Semantic Modeller was available for Preview (and it still is) in July update. We, at Qubix have been involved with testing S

In my blog post Oracle AI Vision: Just like a Box of Chocolates I am discussing how to create a custom machine learning model for pneumonia image classification (can an image provided be classified as normal or infected lungs) using Oracle’s OCI Vision AI service for code-less image classification machine learning model.  In this post, I am taking the model created during my previous exercise and plugging it into Oracle Analytics in order to make predictions from there using self-service Oracle Analytics tools such as Data Flows and Data Visualisation. But before we can start using OCI Vision models in Oracle Analytics, there is a small amount of setup work to be done. Note : Oracle documentation for Analytics Cloud contains very good guidance on how to set everything up. You can find this document here . My example below mostly follows that document, however use case is different and there are one or two things which the original document is not mentioning (safe domains, plugins). Se