The latest version of Oracle Autonomous Analytics Cloud includes a refreshed data visualization a capability that empowers business users to explore, discover and visualize their data. This marks its fourth year since becoming generally available with Oracle's initial cloud analytics offerings (Business Intelligence Cloud Service and Data Visualization Cloud Service). Oracle Analytics Cloud's data visualization feature addressed that primary shift to user self-service with a data-driven approach to creating compelling analytics.
Let's face it, data visualization in its most rudimentary form has become table stakes with many vendors (like Tableau, Qlik, Microsoft) offering equally good tools. Every data visualization tool provides the basics of self-service. Uses can connect their data sources and create great looking charts, or whatever their mood dictates that day. There is virtually no compelling differentiation between the vendors on this level.
Oracle has taken the next step by heading into autonomous analytics. Going beyond the common capabilities of centralized business intelligence provided by on-premises core BI systems, then going further beyond the now common self-service capabilities provided by current cloud analytics services. Autonomous analytics provides capabilities that assist the end users with finding, compiling, and creating compelling stories powered by machine learning that then describes their findings. Some of these autonomous capabilities have been released into the product already. So, what's coming in this latest version of Oracle data visualization capabilities and what's different from any other data visualization tool on the market?
Oracle Analytics Cloud release version 5 (v5) has many updates in every aspect of the product making it far more flexible with even more capabilities to customize and tune the visualizations to better fit your visual needs. For instance, we introduce great enhancements to our mobile application, entitled "Day by Day." We improved search capabilities and newly recognized query terms including top, bottom, first, last and most for queries like "Who were my top 10 performing customers last quarter?" We also introduce new built-in visualizations that include dynamic heatmap, multiple data layers, 100 percent stacked bar and picto charts (as seen in the images below).
New with v5, the data flows section of Oracle data visualization capabilities introduces smart data types. This autonomous capability allows the tool to recognize certain data types and intelligently make recommendations. For example, if "city" appears in your dataset, other related attributes such as population, GPS location, county associated with that city will be recommended, thus autonomously enriching your data. This data could comprise demographics, weather, and commodity prices that analysts would otherwise usually manually blend from their own personal data sources.
Another aspect is autonomous recommendations for obfuscation of sensitive data columns. Credit card details for example, should never be display in clear text. In some regions, this may breach privacy laws. Therefore, these data columns like credit card numbers or social security numbers are detected and autonomous recommendations will be made to obfuscate those details (i.e., for credit cards) only the last 4 digits remain (as seen in the image below).
There are many such new smart data types available that not only enrich your overall dataset but by removing the otherwise tedious and manual process makes the overall time to decision faster, more efficient.
Along with Smart data types, functions are shipped to the most optimal place to execute them. If you have a data lake then users can continue to create data flows to enrich their data from their big data source, but will experience optimal performance by having all processing executed by the most appropriate big data engine in the most appropriate location.
Incremental data processing means that existing data flows can be rerun on data sources and only new data will be processed and joined with existing data. No need to completely rerun the whole process.
Data flow branching allows users to split their data into subsets that can produce a group of related output results or load that data to multiple different locations (as seen in the image below).
Automatic Oracle application data replication provides the capability for data visualization to take copies of data sets automatically from your favourite Oracle cloud applications and create an analytic sandbox to enrich it and then perform your analytics without any disruption to the main application or the core operational data.
I've only exposed the tip of the iceberg on the changes already set for Oracle Analytics Cloud release 5.
Check out the video below to see these changes in action.
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