Oracle Autonomous Data Warehouse allows businesses to ingest, transform, store, and govern all data from any source to run diverse analytical workloads, from departmental systems to enterprise data warehouses and data lakes, according to the company.

New user-oriented tools cover the loading, transformation and cleansing of data; the guided construction and deployments of machine learning models; and graph modelling and analytics.

Oracle Autonomous Data Warehouse provides support for multi-model, multi-workload, and multi-tenant requirements – all within a single, modern converged database engine – including JSON document, operational, analytic, graph, ML, and blockchain databases and services, Oracle said.

A key focus of the update is the provision of features that make it easier for analysts, citizen data scientists, and line-of-business developers to take advantage of Oracle Autonomous Data Warehouse.

Data from a variety of sources can be loaded and transformed using drag-and-drop tools. From there, anomalies, outliers and hidden patterns can be discovered in the data. Instead of requiring users to know what they are looking for, Oracle Autonomous Data Warehouse identifies factors of potential interest.

The new Oracle Machine Learning AutoML feature provides a no-code user interface for automated machine learning, allowing even non-experts to exploit machine learning. Users can identify the attribute of interest and the system will select the most promising algorithms, explore the effect of changing parameters, and display the results as a leaderboard. Where appropriate, users can opt to use a sample taken from the entire dataset in order to reduce processing time during this exploratory phase.

Data scientists and others who are happy writing Python code can take advantage of Oracle Machine Learning for Python to apply their ML code to warehoused data.

Oracle Machine Learning Services supports the deployment and management of native in-database models and ONNX-format classification and regression models outside Oracle Autonomous Data Warehouse.

Users can create graphs within their data warehouse, query graphs using PGQL (property graph query language) and analyse graphs with over 60 in-memory graph analytics algorithms.

Graph Studio UI makes graph analytics easier for beginners, with the automated creation of graph models, notebooks, integrated visualisations and pre-built workflows for different use cases.

Oracle Autonomous Data Warehouse provides access to data lakes including Oracle Cloud Infrastructure Object Storage and Oracle Big Data Service (Hadoop).

The product is also preconfigured for use with Oracle applications including Fusion ERP, HCM, E-Business Suite, Peoplesoft and JD Edwards. It includes prebuilt dashboards and KPIs, but can be extended to meet particular requirements.

Oracle Autonomous Data Warehouse can be used with a variety of other data and analytics tools, including Talend, Infomatica, Tableau and Qlik, and it provides built-in data loading from AWS, Azure and Google object storage.

Self-managed superannuation fund provider Triple A Super is developing a wealth management system.

Managing director Brian Pedretti said, "We wanted to weave together a tapestry of multiple software platforms synchronised with our own internally developed portals to create customised, hierarchical, permission-based dashboards using both predictive and prescriptive analytics that institutions, dealer groups and advisers can also integrate with their own systems.

"They can then share these with individual clients, enabling them see their investments and rich data analytics details such as how compliant they are, impending issues or opportunities that enhance greater client engagement and growth opportunities.

"Using a mix of Oracle Autonomous Data Warehouse and Apex [Oracle's Application Express development tool], we now have a single environment allowing us to do this, and from which we can continue to surface new developments and continue to improve and revitalise the portals we had previously developed. It's certainly increased our development capability."

Other Oracle Autonomous Data Warehouse customers include Lyft, Vodafone, Aon and Accenture.

IDC research vice president for data management software Carl Olofson said "Our research, based on interviews with several customers around the globe, shows that those Oracle Autonomous Data Warehouse customers have achieved approximately 63% reduced total cost of operations, while increasing the productivity of data analytics teams by 27%, with breakeven on their investment having occurred in an average of five months.

"This ROI included significant productivity gains across data, analytics, and developer teams. While individual customer results may vary, the benefits found in this study are indicative of the kind of improvements that most may expect.

"With these new intuitive integrated tools incorporated in Oracle Autonomous Data Warehouse, it is reasonable to expect that productivity gains will further increase, enabling businesses to achieve an even better ROI."

Oracle Autonomous Data Warehouse still allows transactional, analytics, machine learning and other data to be stored in a converged SQL database, and is supplied as a single product so there is no need to acquire software for each function.

The 'autonomous' aspect is that it minimises the need for human administrators, resulting in lower cost, improved reliability and better security,