This post explores how to create standard financial reports in Oracle Analytics Cloud (OAC) using governed financial data and reusable reporting structures. It walks through the key modeling and reporting concepts needed to produce familiar financial statements while taking advantage of OAC’s analytical capabilities. The result combines traditional financial reporting requirements with the flexibility of a modern analytics platform.
This is summary blog post with references to posts published under Oracle Analytics Semantic Modeler theme.
This is summary blog post with references to posts published under Unlocking Oracle Analytics Cloud with AI theme.
Introduction A new paradigm is reshaping business analytics. Instead of the traditional build a dashboard, then...
Read more
This year, I had the pleasure of joining the UKOUG Discover 2025 conference — a fantastic gathering of Oracle professionals, engineers, a...
Read more
Unlocking Oracle Analytics Cloud with AI: MCP for Oracle Analytics The Challenge: Bridging gaps between AI and Enter...
Read more
Between 15th and 18th October 2025, I had the opportunity to speak at the HROUG 2025 conference, held on the beautiful island of Crveni oto...
Read more
The Autumn MakeIT 2025 Conference event took place on October 14th, 2025 , at the Computer Museum in Ljubljana . Its unofficial theme, NO ...
Read more
In part 9 of the Semantic Model Series, I dig into data fragmentation in Oracle Analytics — splitting large fact tables like yearly revenue into separate logical table sources instead of one giant table. I walk through setting up fragmentation content filters, and compare the "Combine with Other Fragmented Sources" and "Enable Data Driven Fragment Selection" options with real generated SQL to show exactly how each one changes query behavior.
In part 8 of the Semantic Model Series, I show how to implement dynamic row-level security in Oracle Analytics using data filters at the Business Model level. I walk through setting up users, groups, and application roles, then applying data filters tied to session variables so access to regions like Americas and EMEA is enforced automatically — no per-report filtering needed.
In part 7 of the Semantic Model Series, I cover the three types of variables in Oracle Analytics Semantic Modeler — session, global, and static — and how each is created through initialization blocks. I show practical examples of testing each variable type in analyses, and set the stage for using session variables to implement row-level security at the model level.
In part 6 of the Semantic Model Series, I cover how to build calculations in Oracle Analytics' logical layer — from simple measures like Profit and Profit Margin %, to Time Series calculations (AGO, TODATE) and Level-Based Measures for market-share style analysis. I also walk through reorganizing the Presentation Layer into logical folders and testing everything after deployment.
In part 5 of the Semantic Model Series, I cover the Presentation Layer in Oracle Analytics Semantic Modeler — subject areas, presentation tables, columns, and hierarchies that shape what end users see. I walk through creating a subject area, dragging in logical tables, running a consistency check, deploying the model, and testing it in a live workbook.
In part 4 of the Semantic Model Series, I dive into the Logical (Business) Layer in Oracle Analytics — how it abstracts physical data into business-friendly facts and dimensions. I walk through building fact and dimension tables, creating logical joins and hierarchies, and configuring time dimensions with chronological keys so time-series functions like AGO and TODATE work correctly.
In part 3 of the Semantic Model Series, I cover the Physical Layer in Oracle Analytics — the foundation that connects to source databases and defines tables, aliases, and joins. I walk through creating connection pools, importing fact and dimension tables, building a custom table from a SQL query, and setting up table aliases and joins to prepare the model for the Logical Layer.
In part 2 of the Semantic Model Series, I walk through creating a new semantic model in Oracle Analytics — choosing between an empty model, RPD import, deployed-model import, or Git clone — and reviewing the six main tabs: Connections, Physical Layer, Logical Layer, Presentation Layer, Variables, and Invalid Files. I also cover setting up the connection used to build out the model in later parts.
In part 1 of the Semantic Model Series, I explain the difference between Regular Connections (for data retrieval and analysis) and System Connections (required for building semantic models) in Oracle Analytics. I walk through creating both connection types step by step, including a tip on keeping connection names short for better compatibility with the Semantic Modeler.
It’s Finally Here! Oracle Analytics AI Assistant has been available for a few months, but until recently, I hadn’t had the opportunity to ...
Read more