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Oracle Analytics Cloud and Server

Oracle Analytics Cloud and Server Roadmap

The following is intended to outline our general product direction. It is intended for information purposes only and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle's products may change and remains at the sole discretion of Oracle Corporation.

For release announcements covering features that have been delivered, please check our What's New page or the Oracle Analytics Cloud documentation.

See the Roadmap

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Data connectivity

Q1 CY2021
  1. 1.  Support for generic Java Database Connectivity (JDBC) data sources
    Extended support for the JDBC connection type lets you configure unlimited access from Oracle Analytics Cloud to other JDBC connections.
  2. 2.  Connectivity to Google BigQuery
    Connect to Google BigQuery from Oracle Analytics Cloud to create live or snapshot-based datasets.
  3. 3.  Impersonation support for big data source
    When creating a connection to Hive, the option to use Kerberos authentication type and impersonation details is supported. The new impersonation capabilities allow you to configure authentication using Kerberos and leverage impersonation to designate some users to act on behalf of a different user.
Q2 CY2021
  1. 1.  Connectivity to Microsoft Azure Synapse
    Connect directly to Microsoft Azure Synapse to create live or snapshot-based datasets.
  2. 2.  Support for analytics views from subject areas
    Oracle Database analytics views can be mapped into the semantic layer and consumed by every Oracle Analytics experience.
Q3 CY2021
  1. 1.  Direct connector to REST APIs (JSON-based)
    The ability to use REST API endpoints as a data source for datasets will be supported. This capability lets you query existing web service URLs that provide endpoints to authorized users.

Data modeling and preparation

Q1 CY2021
  1. 1.  Scheduled refresh for datasets
    Dataset snapshots will offer a custom refresh schedule to retrieve fresh data from data sources and applications based on a predefined and predictable schedule.
  2. 2.  Introduce custom knowledge into the enrichment knowledge base
    Oracle Analytics provides the ability to recommend enrichments and transformation for various dataset columns based on built-in knowledge and a machine-learning–based data profiling engine. Custom knowledge will allow you to introduce your own enrichment sources into the knowledge system. This capability will automatically introduce these sources into the profiling engine which will generate appropriate recommendations when similar patterns are identified.
  3. 3.  Advanced self-service datasets
    Expansion of the existing datasets to include multiple tables and defined join relationships as well as tables for heterogeneous sources within a single join diagram saved as a single dataset that can be shared and collaborated with across multiple users.
Q2 CY2021
  1. 1.  Data quality insights
    Data quality issues within datasets sometimes surface only after the data is visualized and explored. In some cases, the issues remain unseen unless specifically queried. Oracle Analytics data profiler can automatically identify quality issues and present a quality insight for each column that surfaces any possible issues. You can then fix the issues interactively and have greater confidence in your analysis.
  2. 2.  Increased limits of rows processed by data flows
    Increase row limits that can be processed by data flows.
Q3 CY2021
  1. 1.  Web-based semantic modeler
    Newly designed semantic modeler to create semantic layers on top of federated sources. With full compatibility with existing remote desktop protocols (RPDs), the new semantic modeler introduces a modern experience and focus on team development, version control, and lineage analysis.
  2. 2.  Semantic model markup language
    With the introduction of the new semantic modeler, Oracle Analytics will also announce the semantic model markup language (SMML). This open, JSON-based format will allow semantic developers and third-party tools to create and modify Oracle Analytics semantics models using standard editors, codes, or scripting languages. This open format will open the possibilities for higher level of integration with homegrown and third-party applications.

Data visualizations

Q1 CY2021
  1. 1.  Support for subject area hierarchies in self-service pivot and table
    Within Oracle Analytics' self-service data exploration capabilities, you will have the ability to drill up and down predefined subject area hierarchies.
  2. 2.  Access to external map backgrounds
    Integrate maps hosted on a web server as a map background in your visualizations using the web map service (WMS) protocol and XYZ tile layers. These map backgrounds seamlessly integrate the map layer into Oracle Analytics Cloud for you to overlay your data visualizations.
  3. 3.  Increased limits on number of rows for data export from analyses and dashboards (classic)
    Increase row limits on various export and download options, such as CSV export, formatted Excel exports, and others. This limit increase will apply to exports from analysis (answers), dashboards, and publisher.
  4. 4.  Multicolumn and no-sort options
    Display your data with greater control using the new sorting capabilities. Visualizations can leverage either the multiple column sorting capability or you can turn off all sorting, depending on the use case.
Q2 CY2021
  1. 1.  Conditional formatting
    Expansion of Oracle Analytics' self-service color management capabilities to be able to define threshold and target-based conditional formatting for every measure, and then apply it to specific visualizations as needed.
  2. 2.  Pivot export to Excel
    Ability to export pivot tables to Excel while retaining the columns/rows layout.
  3. 3.  Flexible, layer-based combo chart
    Combo charts are a powerful tool in Oracle Analytics, offering the ability to mix different chart types into one. We will be adding more chart types that can be mixed together as well as a layer-based approach for organizing and constructing your chart.
  4. 4.  Increased limits on number of rows for data export from project visualizations
    Increase row limits on various data export and download options when exporting data from Oracle Analytics projects.
  5. 5.  Support for Mapbox maps and interaction
    Mapbox provides a high performance and fluid mapping experience to support even the most complex spatial scenarios.
  6. 6.  Disjointed layers on maps
    Independent map layers can come from multiple data sources without the need to join these sources together. These capabilities provide power analysis in a map where your data can overlay multiple layers of geographical information.
  7. 7.  High-resolution printing in data visualization
    Beautifully print your dashboards or visualizations with the WYSIWYG accuracy of your self-service analytics reports.

Augmented analytics

Q1 CY2021
  1. 1.  Text analytics in data flows for Oracle datasets using Oracle Advanced Analytics functions of Oracle Database
    Turn your unstructured data into powerful information with string tokenization capabilities using data flows. These capabilities provide word frequency and natural keys that enable cross drilling into your original datasets.
  2. 2.  Frequent itemset in data flows for Oracle datasets using Oracle Advanced Analytics functions of Oracle Database
    Powerful capability to identify cart frequent item sets using data flows in a simple, easy-to-use experience. This means you can understand how like groups behave, giving you the ability to make buying recommendations or organize a shopping floor to maximize profitability.
  3. 3.  Full model details in Oracle Analytics Cloud inspectors when using Oracle Machine Learning
    When building models using Oracle Machine Learning, attributing outputs are generated that provide the results of how the predictions were made. This becomes very powerful as you can do analysis and build visualizations in Analytics Cloud on this generated output to easily understand the predictions indicators.
Q2 CY2021
  1. 1.  Automated insights
    Auto discovery of key insights based on any dataset will allow analysts to identify key behaviors in their data and quickly create collaborative sorties based on these experiences.
  2. 2.  Improved natural language generation
    Creating narrative reports with natural language generation (NGL) is now even more powerful with the support of multiple attributes and measures using more inputs to generate in-depth analysis of your data. We will introduce additional data awareness to the NLG engine to generate focused insights.
Q1 CY2021
  1. 1.  Out-of-the-box, clean consumer mode for projects
    Presentation mode for your projects will be automatically created, saving you the initial creation time and enabling you to focus on customization and organization.
  2. 2.  Support for multiple selections in a data action
    Data actions now support passing multiple selections and provide control to specify single or multiple selections based on the use case, to ensure the action is invoked accurately.
Q2 CY2021
  1. 1.  Next-Generation Oracle Analytics Mobile
    The next generation of Oracle Analytics Mobile will bring together all the powers of natural language, augmented analytics, and now, access to the data catalog to access any existing data visualization, dashboard, or publisher report.
  2. 2.  Customize actions and options available in consumer mode
    Fine-tune the consumer experience of your projects with detailed design and user interaction options.
  3. 3.  Redwood
    Oracle Analytics will introduce a newly envisioned Oracle Redwood design across the entire platform.

Security and governance

Q1 CY2021
  • 1.  Oracle Analytics Server annual release
    Oracle Analytics provides annual releases for its on-premises/self-managed version of Oracle Analytics Server (OAS). Capabilities will include all features released in OAC 5.6, 5.7, 5.8, and 5.9.
  • 2.  Private access channel
    Access remote data sources located on-premises or on third-party cloud infrastructure or services, securely and in high performance using direct access via a private, secured channel.
  • 3.  Vanity URLs
    You can now use your own URL to access your Oracle Analytics Cloud instance. This provides a friendly name and personalized approach for users to access OAC.