Small businesses looking to use artificial intelligence often face a major obstacle: critical information sits across different databases, cloud platforms, and business applications. Oracle is expanding its Autonomous AI Lakehouse to help organizations connect that information, give AI systems a better understanding of business data, and make more informed decisions without constantly moving data between systems.
Announced October 26 at Oracle AI World in Las Vegas, the new and expanded capabilities for Oracle Autonomous AI Lakehouse include a Business Semantic Layer, expanded support for Apache Iceberg data, centralized security controls, and advanced business simulation tools.
The updates aim to make enterprise data more accessible to employees and AI agents while maintaining consistent definitions, security policies, and governance across different environments.
Key Takeaways
- Oracle is introducing tools that help AI agents understand business terminology, financial metrics, and operational relationships.
- Expanded Apache Iceberg support allows businesses to manage and analyze data across different platforms without unnecessary duplication.
- New simulation capabilities help organizations evaluate financial and operational scenarios before making decisions.
- The AI Data Catalog and Business Semantic Layer are expected to become available within the next 12 months.
Helping AI Understand Business Data
One significant addition is Oracle’s Business Semantic Layer, designed to give AI systems consistent definitions of business metrics and relationships.
For example, different departments might calculate profitability, customer acquisition costs, or inventory turnover differently. The semantic layer helps establish shared definitions that AI agents and analytics tools can use when interpreting information.
Oracle is also introducing an AI Data Catalog that can connect external catalogs, databases, applications, and Apache Iceberg tables without requiring businesses to duplicate the underlying information.
The catalog will use AI to enrich information about data sources, including their business meaning, relationships, and origins. This could make it easier for employees to locate information and ask AI-powered questions about company performance.
“Giving AI access to enterprise data is not enough. To make informed decisions and take trusted action, AI agents need to understand the business context behind that data and operate within an organization’s rules and policies,” said Çetin Özbütün, executive vice president, Autonomous AI Database Technologies, Oracle.
Expanded Data Access and Faster Analytics
Oracle is expanding its support for Apache Iceberg, an open table format commonly used to manage large analytical datasets. Businesses can create, update, and delete Iceberg tables using SQL while taking advantage of features such as historical data access for auditing and recovery.
The company’s Data Lake Accelerator also received performance improvements. In benchmark tests involving 100-terabyte TPC-DS datasets, Oracle reported up to three times faster query execution. The maximum accelerator cluster size has also tripled, although actual performance improvements will depend on individual workloads.
Another addition, Oracle Data Studio, provides an AI-assisted workspace for preparing, managing, and analyzing information. Teams can connect to more than 100 data sources and work through natural-language instructions, SQL, or Python without first provisioning a database.
For smaller businesses without large dedicated data teams, this could reduce some technical barriers to analyzing information spread across accounting, sales, inventory, and operational systems.
Using Business Simulations to Improve Planning
Oracle is also integrating its Essbase technology into Autonomous AI Lakehouse through a Business Simulation Engine.
The capability allows organizations to conduct multidimensional forecasting and what-if analyses using existing business data. A company could evaluate how rising supplier costs might affect profitability, examine alternative inventory strategies, or estimate the financial impact of expanding operations.
Users can ask questions in natural language, generate simulation reports, and perform analysis through Excel.
Oracle highlighted Liberty Energy, a North American energy services company, as an example of how organizations are using the platform to connect information across multiple cloud environments.
“Oracle Autonomous AI Lakehouse gives decision-makers real-time visibility into field revenue, inventory usage, costs, and profitability,” said Rhett Rowan, vice president of IT, Liberty Energy. “It seamlessly brings together Apache Iceberg tables with data from Amazon S3, Amazon databases, Microsoft SQL Server on Azure, MongoDB, and PostgreSQL, making it easy to add new data sources to power AI, analytics, and business simulations.”
Security, Availability, and Costs
The platform includes centralized security controls intended to restrict users and AI agents to authorized information. These controls extend across Oracle and non-Oracle systems, including cloud and on-premises environments.
Oracle Autonomous AI Lakehouse is available through Oracle Cloud Infrastructure, AWS, Microsoft Azure, Google Cloud, Oracle Exadata Cloud@Customer, and OCI Dedicated Region.
However, businesses considering these capabilities should account for implementation complexity, data quality, employee training, and integration requirements. AI-generated insights will still depend on accurate underlying information and clearly defined business rules.
Availability also varies. Oracle expects the AI Data Catalog and Business Semantic Layer capabilities within the next 12 months. The announcement does not provide detailed pricing or a complete rollout schedule for every enhancement. Oracle cautions that future product features and release dates remain subject to change.
For small businesses already managing information across multiple systems, the developments point toward more integrated AI-powered analytics and forecasting. Whether those benefits justify adoption will depend on the cost of implementation, the quality of existing data, and how effectively the tools address everyday operational decisions.
This article, "Oracle Expands AI Lakehouse with New Data and Forecasting Tools" was first published on Small Business Trends