Intro to Oracle Analytics Server

Oracle Analytics Server (OAS) is the latest version of Oracle Business Intelligence Suite Enterprise Edition (OBIEE), which runs on premise and introduces some notable new features like machine learning and artificial intelligence. It also incorporates Oracle DV, which is a new BI reporting tool that is user-friendly and capable of churning out pixel-perfect reports. Prior to the release of on-premise OAS, Oracle DV was available only through Oracle Analytics Cloud (OAC).  In this article, I will detail the features of OAS to distinguish it from existing tools and help potential users understand its value.

Familiar Features

Analytics dashboards

Dashboards are the collection of multiple reports where a business user can get an overall picture of what is going on with the business and also access detailed information whenever needed. With numerous types of visualizations that can communicate real-time data in different ways, managing content with these interactive dashboards is very user-friendly and easy to analyze, filter, and sort.

Enterprise semantic models

In OAS, users rely on Oracle BI Administration Tool for semantic modeling, a change from the Data Modeler or Developer Client Tool used to build semantic models in Oracle Analytics Cloud.

Role-based security

Oracle Analytics Server allows users to share their content with others. Users can give access to specific items in the catalog and to dashboards. This helps control the content that users can view and edit.

Data preparation for analysis

With OAS, users can prepare data sets for analysis. You can cleanse, standardize, and enrich the data set before analyzing the data on a visualization canvas.


Oracle Analytics Server enables users to better explore, visualize, and analyze data, an improvement from the complexity of OBIEE that end users needed to endure in order to build reports. Oracle DV, a feature of OAS, enables users to easily build reports, make complex joins, and load tables. There is no denying that RPD is very useful and is one of the main reasons why OBIEE has historically been preferred over other available BI tools.

OAS builds on OBIEE’s helpful attributes to make it the more attractive tool. With OAS, data sets and data flow can also be created directly by loading the tables in the front-end. Building dashboards and reports is much easier and user-friendly with OAS. The visualizations yielded are more detailed and feature advanced views like tag clouds, treemap, and more.

Sharing improvements

The ability to share reports and visualizations is one of the significant changes in OAS which allows users to export reports to different file formats, share to social media, and email out directly. Users can also schedule emails and print visualizations which is quite easy with the upgrade.

Expansion to mobile devices

Reporting on mobile phones and tablets is one of the most sought-after features for users.  OAS delivers this, which improves accessibility of the reports and data with pixel-perfect visualization and geolocation information. This is particularly helpful for utilities during downtime caused by storms or outages.

Advanced Features with Augmented Analytics

Natural language-based features

Search, navigation, customization, and narration of projects, reports, datasets, and more allow users to sort the home page per their preference.

Data enrichment

Importing the data from different data sources like Essbase, Amazon EMR, Apache Hive, Impala, Spark, etc. leaves the work unfinished, even after creating the data sets or data flows. Data enrichment arranges the data by column-level profiling based on the recommendations. The system automatically detects a specific semantic type during the profile step.

A semantic type is categorized by identifying the data such as date, city, credit card, etc. For example, you might extract the month and year from the date to create a visualization for sales during the last two years. Enrichment of data saves a lot of time and money because users spend a huge amount of time converting the data. A few of the semantic type recommendations involve column concatenations, part extractions, semantic extractions (like area codes from phone numbers), full or partial masking of the column, etc. Semantic types are identified based on patterns found in the data. A general rule is that 85% of the data value in the columns must match the criteria for the system to make recommendations.

Explain and Data Analysis

Explain uses Oracle’s machine learning, which can do wonders for data analysis by quickly generating accurate and powerful information about your data. It can analyze the content of the data and generate a description where it defines and explores the details of the data. Using Explain is very simple. A user can just choose a column in the data set and click on Explain, which automatically analyzes the statistics of the data and gives the most significant information. For example, an organization might have a visualization which shows previous year’s profits from different clients. That organization may have a proposal for a new project with a former/existing client. If the team wants to access data about their previous project to look into all the experience they have had, and see other information about the client, Explain comes in handy.

Explain is very useful for data analysts who might not know what is trending right now and can’t spend a lot of time creating reports or calculations.

Machine learning for predictive analytics

After understanding how Explain uses Oracle’s machine learning, it is helpful to know how predictive analytics rely on machine learning. A few embedded machine learning algorithms are used in analytics predictive models to mine data sets and predict or identify classes of record. These Oracle Analytics predictive models can be created and applied with the data flow editor. Predictive models can also be trained for different applications through algorithms within Oracle Analytics.

What features might no longer be available?

OAS has most of the features as OBIEE with a few exceptions that are no longer supported, which could mean they might be disabled in the future.

Certain features from OBIEE, like Oracle E-Business Suite, blocking analyses in answers, XML-based data sources, BI Scheduler Job Manager, and flash templates will no longer be improved, but they will be supported for the life cycle of this release.  It is not noted whether or not these features will supported in a future release.

In conclusion

Oracle Analytics Server combines an array of new capabilities with many of the features that have traditionally made OBIEE one of the premier BI and analytics services.  OAS offers users stronger functionality in areas like visualization, analysis, and data sharing, among others, which will provide BI teams the ability to more easily and effectively derive valuable insights from their organizational data.

About the Author

Gokuleshwar Raju Nannapuraju

Gokuleshwar Raju Nannapuraju

Gokuleshwar Raju Nannapuraju is a BI/ETL and Infrastructure Consultant at HEXstream. He has several years of experience implementing projects related to OUA, CCMB, WAM/WACS, and OFSC. He graduated from Northwestern Polytechnic University with a Master’s in Computer Science. Apart from work, he enjoys trekking, traveling, and watching sports, and he is an avid motorcycle enthusiast.

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