Oracle today announced Oracle Energy and Water Data Exchange, a new cloud-based solution designed to streamline data integration, sharing, and preparation for AI use cases. Applying an intelligent semantic layer, the offering translates complex raw enterprise and third-party information into a standardized industry model with natural language meaning for easier interpretation and action. This often overlooked, but critical, data preparation step helps ensure that information is not only accessible but also meaningfully contextualized for analytics and AI-driven insights. With the Data Exchange, cloud developers, data scientists, and analysts can significantly fast-track priority projects, including data mining, predictive analytics, and cutting-edge generative AI (GenAI) applications.
“Utilities are under increasing pressure to accelerate digital transformation, however, many struggle with fragmented, siloed, and inconsistently labeled data spread across multiple sources. This makes it difficult to unify information and use effectively to innovate and improve utility operations,” said Hillary Martin, vice president of analytics and innovation, Oracle Infrastructure. “By cleansing, organizing, and adding the appropriate meaning to an organization’s data, Oracle Energy and Water Data Exchange makes data easier to find, understand, and leverage for AI and advanced analytics use cases that can transform raw data into actionable insights.”
Preparing data for action
Many utilities have invested in industry solutions that promise to centralize and unlock the value of data only to be stifled by inconsistent, hard-to-find, and unstructured data. Unlike other solutions, Oracle Energy and Water Data Exchange addresses these foundational problems by focusing on data quality and meaning. Through a series of validation checks, error reporting, cleansing, deduplication, and synchronization processes, the offering helps ensure that data is accurate, easy-to-understand, and ready for analysis, replication, or AI application use.
Oracle Energy and Water Data Exchange features an industry data model that unifies multiple standards—including IEEE and IEC—which helps ensure that when data is ready for production, it can be securely shared across partners to streamline integration, enhance interoperability, and drive operational efficiency. With this clean, validated data, utilities can expedite analytics-based decisions and priority use cases, such as connecting Distributed Energy Resources (DERs) to grid and customer devices to provide a comprehensive view of grid conditions across EV charging locations in a specific area.
AI-ready data
Oracle Energy and Water Data Exchange is focused on unifying data from Oracle and other third-party industry applications. It also can standardize and cleanse public data sources such as the U.S. Department of Energy, U.S. Census Bureau, and National Oceanic Atmospheric Administration (NOAA) for further intelligence.
The solution is built on the security, performance, and scalability of Oracle Cloud Infrastructure (OCI) and takes advantage of a number of OCI services. For example, Data Exchange natively synchronizes with OCI Integration Services to connect any application and data source for automated end-to-end processes, centralized management, and simplified migration to the cloud.
Oracle Energy and Water Data Exchange also leverages the built-in AI Vector Search and in-database machine learning capabilities of Oracle Database 23ai to help eliminate the complexity and cost of integrating and managing multiple databases. This helps maintain data consistency across areas such as smart meters, distribution assets, and utility grids. Unique, language-based semantic representations also make it easy to create embeddings for data, feed GenAI engines, and enhance applications such as smart chatbots.
Oracle Energy and Water Data Exchange is ideal for IT teams, consultants, integration specialists, and solution architects who have been tasked with integrating multiple data sources across an enterprise. The solution makes it easier to manage data quality across sources, enable smooth integration between disparate systems, and address inconsistencies and information misalignments. This helps lead to faster integrations, fewer errors during data migration or system updates, and enhanced solution performance for higher client satisfaction. Oracle plans to expand the solution to include a data sharing framework, enabling secure and efficient sharing of validated data across teams and external partners.
