Oracle Analytics Cloud (OAC) Release 6.0 introduces sophisticated new capabilities that expand access to analytics and accelerate your time to insights, such as Oracle machine learning and database and graph analytics. You’ll appreciate new features that help you quickly assess the state of your data, along with expanded data set capabilities that enable you to combine multiple sources with full join control in an easy user interface. There’s also new data connectivity you can leverage for Google BigQuery and JDBC.
What’s New in Release 6.0
Frequent itemset, as released in Oracle Analytics 5.9, enables you to see groups of products commonly purchased together. You can now generate association rules for these groupings which indicate that for certain groups of products, what additional product is brought. For example you can understand, based on buying behavior, if a customer normally buys bread and milk that they also purchase eggs.
Use the Oracle machine learning model's rich controls for choosing the model type and parameters when you forecast data. You can generate time-series statistics to evaluate the accuracy of your model.
Shortest Path | Docs
Oracle Analytics now provides users with a simplified user interface to access graph data from Oracle Autonomous Data Warehouse to calculate the shortest path from a source to a destination. This type of information is extremely valuable for a business that creates transportation routes or needs to ensure efficiencies when traveling between two locations.
Sub Network | Docs
You can use sub-network graph analytics to utilize relationship data. Ever wonder how social media platforms show you how many immediate contacts you have versus second or third connections? This capability enables you to quickly determine how many hops away your relationships are.
Node Ranking | Docs
Ranking is commonly used in web search engines to determine the best results to display first in a list. If a certain webpage has many other pages that direct traffic to it, this page ranks higher in the analysis than a single page that isn’t reached through other web pages.
Nodes Clusters | Docs
Clustering using graph analytics groups connected relationships into a cluster that has a relationship in both directions between the source and destination. When the relationship between two vertices doesn’t go in both directions, these points reside in separate clusters.
There are new advanced data modeling capabilities for self-service data modelers. You can now work with data sets that utilize multiple tables from heterogeneous sources with defined join relationships. This uses a single join diagram saved as a single data set. You can define cardinality between joins to ensure your data is aggregated accurately.
Data Quality Insights | Docs
Whether you’re importing data from files or connecting to existing sources, Quality Insights accelerates getting your data ready for analysis. Leveraging the power of the semantic profiler in Oracle Analytics, Quality Insights provides a visual representation of your data's quality, helping you rapidly identify issues. These indicators are based on null values, data type inconsistencies, and semantic type classifications. Each column in your data has a graph to show the distribution of the represented data. You can make inline edits to quickly address any issues, rename columns, and use the scrollable mini map to easily traverse long lists.
Custom Knowledge | Docs
Every business has standard metadata that applies to them. Administrators can now import this information into the Reference Knowledge section of the console for all users to leverage. The semantic profiler in Oracle Analytics will incorporate both the system knowledge that exists today and the business metadata that’s added to the enrichment recommendations, increasing accuracy and providing better context during data preparation.
Connect to JDBC Data Sources | Docs
JDBC connectivity opens up a wealth of sources to you. Using Remote Data Gateway (RDG), you can configure the JDBC drivers to allow Oracle Analytics to connect to data sources for cached data. This connection type supports data sets with multiple connections so you can easily integrate this data with other data in your business.
Connect to Google BigQuery | Docs
Create connections to your Google BigQuery sources, natively through self-service analytics. Users can create and configure these connections to reach the data they need quickly.
Scheduled Data Set Reload | Docs
Data sets may not always need to be static or live and manually refreshing them can take time. Now you can configure your data sets to be refreshed based on a static date and time or set repeat schedules to occur based on your needs.
Traverse your data like never before with support for hierarchy navigation of your subject areas or multidimensional sources such as Essbase or EPM. Defined hierarchies are now displayed in the self-service data navigation panel allowing you to drill up and down into your pivot or table visualizations. Unbalanced, ragged, and parent-child hierarchies are all supported.
Weighted Average in Waterfall Visualizations | Docs
Waterfall visualizations continue to evolve in Oracle Analytics. With this release, you can use weighted averages as a value in the grammar panel. This enables you to display the variation between the categorical values represented on the x-axis. In some cases, this shows a better representation with a percentage change as compared to a discrete value change.
Geospatial analysis is a powerful tool to visualize geographic data. Many times, you’ll have unrelated data sets that you want to compare on the same map. With non-joined data sets, you can overlay unrelated map data and see how the points correlate to each other.
Other notable features include: