Oracle NetSuite has announced the availability of NetSuite Analytics Warehouse, which is built on Oracle Analytics Cloud and Oracle Autonomous Data Warehouse. The NetSuite Analytics Warehouse helps customers spot patterns and quickly surface insights from NetSuite and third-party data to enhance decision making and uncover new revenue streams.
“Organizations have access to more data than ever before, but manually collecting and analyzing it on spreadsheets or multiple visualization tools is time-consuming, error-prone, and slows down decision making,” said Evan Goldberg, EVP, Oracle NetSuite. “With NetSuite Analytics Warehouse, our customers can now take advantage of a complete, prebuilt analytics solution that accelerates decision-making and enables their organizations to quickly respond to changing customer needs and new market opportunities.”
NetSuite Analytics Warehouse extends NetSuite SuiteAnalytics, which provides embedded real-time reporting and metrics dashboards using NetSuite data, enabling customers to enrich their analyses with data beyond NetSuite on a single analytics platform.
The first and only prebuilt data warehouse and analytics solution for NetSuite customers, NetSuite Analytics Warehouse offers:
- Easy and secure data transfer: NetSuite Analytics Warehouse is automatically connected to a customer’s NetSuite environment using prebuilt, secure data pipelines. Within minutes, transfers of NetSuite data are scheduled, eliminating error-prone and time-consuming manual data integration projects.
- Enriched analytics with third-party and NetSuite data: Easily extract, transform, and load data from multiple sources, from spreadsheets to unstructured data, without coding. Whether using generic connectors or the 25+ prebuilt connectors to platforms such as Dropbox, Salesforce, and Google Analytics, NetSuite Analytics Warehouse simplifies data and analytics.
- Ready to use role-based analytics: With prebuilt metrics and KPIs, all employees can analyze historical data and spot trends and outliers by leveraging embedded machine learning to obtain more accurate predictions and make recommendations based on their findings.